In multimedia applications, Federated Learning (FL) has emerged as an effective training paradigm, enabling distributed clients to collaboratively train a shared model without transmitting raw data. However, FL remains vulnerable to privacy threats such as data reconstruction and membership inference attacks, which has motivated the adoption of secure aggregation protocols to protect client model updates. Existing secure aggregation schemes that combine homomorphic encryption with secret sharing predominantly follow a sharing-decryption paradigm, requiring additional client interaction during decryption and thereby incurring substantial computational, synchronization, and communication overhead. In this paper, we propose Threshold Vector Aggregation (TVA), a novel secure aggregation protocol that adopts a sharing-encryption and aggregation paradigm. Under TVA, each client encrypts its local update only once using a unique private key and uploads a single ciphertext to the server, without any further interaction. The cipher-texts are directly aggregatable yet individually undecryptable, and only the final aggregated result can be correctly recovered by the server. Extensive experiments demonstrate that TVA significantly outperforms state-of-the-art secure aggregation schemes, achieving up to 33× lower server-side aggregation time, 132× faster client-side encryption, and 170× reduction in communication overhead, while preserving model accuracy comparable to plaintext federated learning.

Mare Nectaris (15.2°S, 34.6°E) is a typical multiring impact basin on the lunar surface. This letter uses data from the Chang’E-2 Lunar Microwave Sounder (CELMS), Clementine UV-VIS, and LRO Diviner to provide insight into the underlying factors responsible for the observed thermal anomalies. The results are as follows: (1) This letter identifies atypical brightness temperature (TB) anomalies in the eastern regions of Mare Nectaris, featuring lower TB during the daytime and higher TB at nighttime. These anomalous areas closely coincide with the distribution of olivine-rich regions. Olivine has a lower dielectric constant compared to its surroundings, and the TB anomalies may be associated with this lower dielectric constant. (2) We identified the distribution and primary causes of typical TB anomalies in Mare Nectaris, highlighting an anomalous region in the northwest. The anomaly in this region is primarily driven by albedo. (3) We also identified cold spots within three impact craters and revealed a correlation between these cold spots and the lunar surface rock abundance (RA) distribution.

The overfitting of domain signals results in poor domain generalization of face anti-spoofing. The current methods usually improve the diversity of source domains to alleviate this overfitting. However, this benefit is minimal, as even the most diverse domain signals will also be absent in the target domain. In this work, we propose a Domain-Guided Prompt Distribution Learning (DGPDL) built on Vision-Language Models like CLIP, which explores a unified representation of domain signals as a prompt across the source and target domain to alleviate the understanding bias caused by domain gaps. Specifically, we first define a learnable Domain-Specific Distribution (DSD) that covers as many domain elements as possible, such as image quality, color tone, camera settings, etc., which establish connections between different domains and linearly combinable prompt in any domain; Then, based on the style statistics of the given sample, we construct its optimal Domain-Specific Prompts (DSPs) from the defined DSD through the designed Prompt Assemble Attention (PAA) with the similarity matching; Finally, the assembled DSPs will act as carrier or agent to perform on both the vision and language branches, synergistically improving the model's recognition of domain signals. By using the prompt to represent domain signals uniformly, if the model can be robust to DSPs in the source domain, it should be applicable to target domain, as they share the same DSD. By representing domain signals as prompts rather than instantiation features, DGPDL effectively reduces the reliance on specific domain appearances. This design enables the model to dynamically adapt to unseen target domains without the need for retraining. Extensive experiments show that the DGPDL is effective and outperforms the state-of-the-art methods on several cross-domain benchmarks.

Embedding watermarks in the diffusion latent space improves robustness but often alters visual content due to the distribution shift between watermarked and clean latent variables. To address this issue, stable latent integration watermark (SLIM) is proposed in this paper, in which watermarks are integrated into the features output by the noise prediction network of a diffusion model, while ensuring that the perturbation introduced in the diffusion latent space remains negligible. Specifically, a watermark encoder–decoder is first trained to convert binary watermark sequences into watermark latent variables that are dimensionally compatible with the diffusion latent variables, enabling flexible and reliable embedding and extraction. The watermark latent variables are processed through the first down-sampling block of the denoising U-Net, and the resulting watermark features are fused with the block output to minimize interference with image semantics. To counteract the perturbations in diffusion features induced by watermark embedding and to ensure accurate watermark extraction, the denoising U-Net is efficiently fine-tuned using a low-rank adaptation module. Experimental results demonstrate that SLIM achieves superior generation quality while exhibiting exceptional robustness against diverse attacks compared with baseline methods. Code will be available at https://github.com/XiaoxiKong/SLIM.

Inverse problems in medical imaging, such as undersampled magnetic resonance imaging (MRI) and sparse-view computed tomography (CT) reconstruction, are essential yet challenging tasks for achieving accurate and reliable diagnostic images. Traditional reconstruction approaches, including iterative optimization algorithms and supervised deep learning methods, often struggle with limited adaptability across imaging protocols, substantial computational requirements, and poor generalization between different imaging modalities. Diffusion-based generative models have recently demonstrated promising results; however, these methods frequently suffer from cumulative estimation errors in their sampling processes, limiting their practical performance and robustness. In this paper, we propose a novel framework called Parallel Trajectory Constrained Sampling (PCS), which substantially enhances image reconstruction quality by explicitly enforcing consistency with the underlying physical measurement process. Specifically, PCS introduces a measurement-domain diffusion model whose reverse stochastic differential equation (SDE) trajectory is analytically determinable, thus obviating the need for a learned score estimator within the measurement domain. Furthermore, a parallel trajectory constraint is formulated to rigorously align the reverse sampling paths of the measurement and image diffusion processes, ensuring strict adherence to the known physical model at every sampling step. The proposed PCS method is flexible and can seamlessly integrate various SDE-based diffusion priors. Extensive experiments on representative inverse problems—including undersampled MRI reconstruction, sparse-view CT reconstruction, and image super-resolution—demonstrate that PCS consistently outperforms existing state-of-the-art diffusion-based reconstruction methods. Although current evaluations focus specifically on MRI and CT modalities, the PCS framework holds considerable promise for broader applicability to other imaging modalities and inverse problems, which we plan to investigate in future studies.

Reversible data hiding is essential for data security and integrity in industrial environments. However, existing methods suffer from unstable prediction errors and limited embedding capacity. This article proposes an adaptive reversible data hiding framework for industrial images using enhanced content pixel prediction error. Initially, a refined content pixel selection method is proposed to accurately identify embeddable pixels in industrial images. Then, a distance-based pixel prediction scheme is developed that utilizes high-relevance content pixels for improved prediction accuracy. In addition, a content-adaptive embedding strategy is introduced that prioritizes smooth blocks while employing prediction error pair mapping to simultaneously maximize embedding capacity and minimize distortion. Experimental results show that the proposed method outperforms state-of-the-art approaches in embedding capacity, achieving an average of 45 625 bits per image with the capability to embed in complex textured regions. Extensive evaluation on standard benchmarks and two industrial image databases validates its effectiveness for secure industrial data transmission.

The appearance information of the target has been used as the only tracking cue for most trackers to locate the target in the video. However, when the surrounding environment changes drastically or there are similar interference targets, it usually causes target drift. We propose a tracker named MRTrack, a transformer-based spatiotemporal information de coupling network architecture to enhance the target tracking capability. We designed two training schemes to explore the effective integration of motion cues derived from optical flow with representation information. The first approach integrates the target's motion and representation information for training. The second scheme is a step-by-step training, where the target motion information is first learned, and the learned model is used for representation learning. We compare the two training methods on five generic tracking datasets. The experiment results indicate that the first training approach can better integrate motion and representation information, leading to more precise tracking results for MRTrack compared to solely relying on the appearance model. In addition, optical flow cues are used only in the training phase to guide the tracker in understanding motion information, and no additional cost is incurred during tracking inference.

The differences in imaging devices cause multimodal images to have modal differences and geometric distortions, complicating the matching task. Deep learning-based matching methods struggle with multimodal images due to the lack of large annotated multimodal datasets. To address these challenges, we propose XCP-Match based on cross-modality completion pre-training. XCP-Match has two phases. (1) Self-supervised cross-modality completion pre-training based on real multimodal image dataset. We develop a novel pre-training model to learn cross-modal semantic features. The pre-training uses masked image modeling method for cross-modality completion, and introduces an attention-weighted contrastive loss to emphasize matching in overlapping areas. (2) Supervised fine-tuning for multimodal image matching based on the augmented MegaDepth dataset. XCP-Match constructs a complete matching framework to overcome geometric distortions and achieve precise matching. Two-phase training encourages the model to learn deep cross-modal semantic information, improving adaptation to modal differences without needing large annotated datasets. Experiments demonstrate that XCP-Match outperforms existing algorithms on public datasets.

Current image fusion methods struggle with real-world composite degradations and lack the flexibility to accommodate user-specific needs. To address this, we propose ControlFusion, a controllable fusion network guided by language-vision prompts that adaptively mitigates composite degradations. On the one hand, we construct a degraded imaging model based on physical mechanisms, such as the Retinex theory and atmospheric scattering principle, to simulate composite degradations and provide a data foundation for addressing realistic degradations. On the other hand, we devise a prompt-modulated restoration and fusion network that dynamically enhances features according to degradation prompts, enabling adaptability to varying degradation levels. To support user-specific preferences in visual quality, a text encoder is incorporated to embed user-defined degradation types and levels as degradation prompts. Moreover, a spatial-frequency collaborative visual adapter is designed to autonomously perceive degradations from source images, thereby reducing complete reliance on user instructions. Extensive experiments demonstrate that ControlFusion outperforms SOTA fusion methods in fusion quality and degradation handling, particularly under real-world and compound degradations.

Mare Insularum (7°S–18°N, 0°–38°W), a basaltic lunar mare enriched in FeO + TiO2 abundance (FTA) and characterized by numerous impact craters, exhibits notable brightness temperature (TB) anomalies. This study used TB data from the Chang’e-2 Lunar Microwave Sounder (CELMS), in conjunction with FeO and TiO2 data, to investigate the spatial distribution of thermal anomalies and their geological origins. Two prominent hot regions were identified within Mare Insularum, attributed to variations in FTA, highlighting the influence of compositional heterogeneity on surface thermal behavior. In addition, three cold spots near the Hortensius crater were analyzed using contour plots of rock abundance (RA) and TB. The maps reveal a strong inverse relationship, where higher RA values correspond to significant reductions in TB. This pattern underscores the role of RA enrichment in driving thermal anomalies by enhancing radiative cooling as a result of altered regolith thermal properties. These findings provide new insights into the interplay between compositional factors and thermal dynamics on the lunar surface, advancing our understanding of the thermal evolution of Mare Insularum.

Mare Serenitatis (28°N, 17.5°E) has undergone intricate volcanic events, leading to the deposition of basaltic lava flows from various stages in the basin. This study presents prospective geological insights into the mare basalts within Mare Serenitatis by using data from the Chang’E-2 Lunar Microwave Sounder (CELMS), thereby aiding in enhancing comprehension of magma dynamics, thermal evolution, and volcanic activities. The following are the results obtained from this study: 1) the potential geological information in Mare Serenitatis was analyzed using brightness temperature (TB), identifying potential connections between deep-seated units within the basin; 2) the distribution and causes of TB anomalies in Mare Serenitatis were investigated, revealing that daytime hot anomalies mainly occur at its southern rim, with TiO2 abundance (TA) being the primary influencing factor. The nighttime cold anomalies appear near several craters and extend with depth; and 3) an untypical TB anomaly was observed in the central region of Mare Serenitatis, exhibiting lower TB at daytime and higher TB at nighttime. This study suggests the presence of a material with a lower loss tangent on the surface of the central region of Mare Serenitatis and suggests that this material is related to Mg-rich rock.

Based on Chang’e-2 lunar microwave sounder (CELMS) data, this letter reveals a significant lunar cold spot in the Pierazzo impact crater (3.3°N, 100.24°W) on the far side of the moon. By integrating brightness temperature (TB) distribution, surface parameters [ilmenite content, rock abundance (RA), albedo], and loss tangent, we obtain these following results: First, we identify Pierazzo crater as a cold spot, exhibiting significantly lower nighttime TB across all four frequencies compared to its surroundings. Then, this TB anomaly is primarily influenced by a high loss tangent value, which indicates that regolith porosity increases with depth due to rock fragmentation induced by impact, thereby enhancing the thermal resistance effect. Next, this thermophysical anomaly exhibits secondary contributions from albedo and RA. Finally, asymmetric thermal response between eastern and western regions suggests subsurface material heterogeneity.

Diffusion models show great potential in solving inverse problems, including MRI reconstruction. With its unique characteristics, medical imaging demands both efficiency and accuracy in the reconstruction process. However, existing MRI reconstruction methods based on diffusion models often fall short of fully leveraging the available measurements during sampling. Consequently, these methods suffer from compromised reconstruction quality and elevated bias, especially when dealing with large acceleration factors. In response to these challenges, we propose Dual Manifold Constraints (DMC), a fast MRI reconstruction method based on diffusion models. We treat the sampling process as a combination of denoising and adding noise processes, and we constrain these two processes using both pristine measurements and their noisy counterparts to adapt to the geometry of diffusion. It’s worth noting that we propose a method to estimate the noisy measurement that satisfies the sub-sampling process to maintain the current data manifold when performing data consistency constraints. Experimental results show that our method outperforms the latest diffusion-based methods regarding both reconstruction speed and accuracy, and exhibits strong out-of-distribution generalization performance.

Non-blind rotary motion deblurring (RMD) aims to restore a latent image from its blurred image. Since the integration path of rotary motion blurring (RMB) is a circle, RMD is modelled as a typical motion deblurring in the polar coordinate system (PCS). However, existing PCS-based methods use hand-designed image priors and are limited by transformation errors, including Cartesian-to-polar transformation (CPT) error and polar-to-Cartesian transformation (PCT) error. In this paper, we analyze the impact of transformation errors on the restored image and propose a novel end-to-end network which introduces a convolutional neural network (CNN) to learn image priors. Specifically, considering the CPT error, we construct a degradation model and solve it in an unrolling way, effectively reducing the ringing artifacts. For the PCT error, we develop a PCT error correction module (PCM) to reconstruct the lost details and textures. Experiments show our method performs against state-of-the-art (SOTA) approaches on synthetic and real-world rotary motion blur datasets by a large margin. The code and model are available athttps://github.com/Jinhui-Qin/RMD_PCS.

Brightness temperature (TB) data from the Chinese Chang’E-2 (CE-2) microwave radiometer (MRM) are constrained by the limited quantity of the original dataset, which cannot express global TB distribution. In order to construct the lunar TB model with the TB data obtained by the MRM on board CE-2, we propose a novel spherical geometric B-spline (SGB-spline) model. The model fully integrates the observed TB data with the lunar geometric features and determines optimal fitting parameters through a subdivision-based optimization process. More specifically, the establishment of the lunar TB model begins by employing spherical area coordinates (SACs) for CE-2 TB data representation across all four frequency channels, followed by applying geometric B-splines to refine the TB distribution. At the same time, it preserves the geometric integrity of the Moon. We observed that the SGB-spline model constructs the more comprehensive TB models during both lunar daytime and nighttime in the 3D Euclidean space, providing a more detailed representation of the spherical spatial information and the effect of frequency channels. Experimental results demonstrated that the proposed SGB-spline model significantly outperforms representative interpolation approaches.

Underwater exploration is crucial for geoscience and remote sensing, but the capture of underwater images is compromised by the degradation of light absorption and scattering. This article proposes a diffusion-color-guided framework (DCGF) to enhance the quality of underwater images and address color deviations caused by randomness in general diffusion models during underwater image restoration. In DCGF, the diffusion model reconstructs the image distribution, while a color correction module ensures accurate color representation. A conditional image guides the denoising procedure, aligning the diffusion trajectory closely with the target domain. This approach reduces the impact of diffusion variability and minimizes deviations. Once a predetermined denoising threshold is reached, the color correction module extracts salient characteristics of color distribution from luminance and RGB channels, enhancing overall efficacy. The experimental results demonstrate that the DCGF algorithm effectively restores degraded underwater images with robustness and effectiveness. The method successfully corrects color degradation and recovers details in low-light conditions, significantly improving underwater image quality.

The Chang’e-2 (CE-2) microwave radiometer (MRM) brightness temperature (TB) observations have significantly enhanced our understanding of lunar subsurface thermal features. While existing TB mapping approaches rely heavily on interpolation methods, this study proposes an advanced framework that combines heat conduction with multilayer microwave radiative transfer to generate continuous TB distributions over 24 hours. The proposed approach incorporates key lunar thermophysical parameters to ensure physical consistency in TB simulations. A spatiotemporal integration and validation scheme was also proposed to enhance data quality. Systematic biases between observed and modeled TB values were then addressed through a calibration approach using sine functions. The simulated TBs align well with the CE-2 MRM observations at both 19.35 GHz and 37.0 GHz. The approach accurately captures global TB patterns while preserving local thermal signatures across diverse geologic units. Detailed analyses at CE landing sites further validate the model’s capability to capture fine-scale thermal variations. The improved spatiotemporal coverage and accuracy of these TB maps provide valuable insights into lunar thermal and geological processes.

Brightness temperature (TB) derived from micro- wave radiometers (MRMs) onboard China’s Chang’e (CE) satellites has provided significant insights into the Moon’s subsurface thermal conditions and evolution. However, conventional TB mapping techniques emphasize spatial correlations among observational data points while largely neglecting the influence of inherent lunar surface factors. In this study, we propose a novel TB estimation approach utilizing geographically neural network weighted regression (GNNWR) combined with multisource lunar remote sensing data to generate TB maps at a higher spatial resolution of$0.0625^{\circ } \times 0.0625^{\circ }$. This method integrates crucial lunar surface parameters in heat conduction and radiation transfer models in a new framework, thereby reducing the risk of overestimation associated with high-resolution targets in sparsely distributed samples. In addition, by replacing the traditional geographically weighted regression (GWR) kernel with a spatially weighted neural network (SWNN), the model effectively addresses spatial nonstationarity and heterogeneity present in TB data and microwave radiative transfer. Comparative analyses demonstrate that the GNNWR approach achieves superior performance, as evidenced by the highest$R^{2}$and the lowest mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). Furthermore, the generated TB maps demonstrate strong alignment with observed spatial trends. These maps also reveal fine-scale thermal features typically obscured by conventional interpolation methods, enhancing their utility for microwave thermal emission analysis and geological studies.

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://github.com/Liling-yang/SDSFusion.

RGB-Thermal semantic segmentation provides reliable support for intelligent traffic perception systems, such as road safety monitoring and autonomous driving perception, by fusing visible and thermal imaging modalities under adverse weather conditions and low-light environments at night. However, the scarcity of multimodal data and the high cost of annotations severely limit the generalization capability of traditional models. To address the core demands of urban scene segmentation, we propose a Hierarchical Matching Guided Feature Enhancement Network (HMFENet) tailored for few-shot learning. It tackles two major challenges: 1) scale diversity of traffic objects (e.g., vehicles and pedestrians) under limited labeled data, which significantly degrades segmentation accuracy; 2) information redundancy across multimodal features, which undermines the enhancement effect of the thermal modality on traffic object segmentation. HMFENet employs a hierarchical dense matching mechanism to establish multi-scale and multi-level feature alignment between query images and support samples. Additionally, it incorporates a mutual information minimization constraint to optimize cross-modal complementarity, thereby enhancing segmentation robustness in complex urban scenes. Experiments on the urban scene dataset, Tokyo Multi-Spectral-$4^{i}$demonstrate that the proposed method achieves state-of-the-art results: an improvement of 5.9% and 9.4% in mean mIoU for critical traffic objects under 1-shot and 5-shot settings, respectively, compared to baseline models. Furthermore, the complementary effect of the thermal modality contributes to a 2.5% improvement under the 1-shot setting. The proposed method provides a feasible solution for deploying multimodal traffic perception systems with low annotation costs. The source code is available athttps://github.com/Zhou-xy99/HMFENet.

To provide an image compression method with better compression performance and lower computational complexity, a new image compression algorithm is proposed in this paper. First, a double-layer non-uniform partition algorithm is proposed, which analyzes the texture complexity of image blocks and performs partitioning and merging of the image blocks at different scales to provide a priori information that helps to reduce the spatial redundancy for subsequent compression against the blocks. Next, by considering the multi-transform cores, we propose an adaptive U transform scheme, which performs more specific coding for different types of image blocks to enhance the coding performance. Finally, in order that the bit allocation can be more flexible and accurate, a fully adaptive quantization technique is proposed. It not only formulates the quantization coefficient relationship between image blocks of different sizes but also further refines the quantization coefficient relationship between image blocks under different topologies. Extensive experiments indicate that the compression performance of the proposed algorithm not only significantly surpasses the JPEG but also surpasses some state-of-the-art compression algorithms with similar computational complexity. In addition, compared with the JPEG2000 compression algorithm, which has greater with higher computational complexity, its compression performance also has certain advantages.

Lacus Mortis has been clarified as the landing site of the Astrobotic Mission One lander mission. In this work, the Chang’E-2 microwave radiometer data was first introduced to evaluate thermophysical features of surface deposits in Lacus Mortis. Several findings are noted as follows. (1) Microwave radiometer data shows abnormal thermophysical parameters existing in shallow layer of mare surface. (2) In crater unit, a strong relationship between microwave performances and other parameters including rock abundance and elevation is shown.

Mare Marginis (13.3 °N, 86.1 °E) is situated on the lunar nearside with rich geological diversity. This letter analyzes the brightness temperature ($T_{B}$) features of Mare Marginis through the normalized brightness temperature ($\text{n}T_{B}$) performances and brightness temperature difference ($\text{d}T_{B}$) using data from the Chang’E-2 Lunar Microwave Sounder (CELMS), in combination with slope, rock abundance (RA), and (FeO + TiO2) abundance (FTA). The results are as follows. First, the$\text{n}T_{B}$of Mare Marginis is relatively high at both daytime and nighttime. The daytime$\text{n}T_{B}$shows consistency with FTA distribution. Second, the$\text{d}T_{B}$of Mare Marginis is basically consistent with the FTA distribution, the consistency decreases as the frequency increases. Third, the correlation analysis shows that slope and FTA are the main factors influencing$\text{n}T_{B}$. In addition, the high substrate temperature is identified as the primary factor responsible for nighttime$\text{n}T_{B}$anomalies of Mare Marginis, whereas the high RA accounts for the emergence of the cold spot phenomenon at Goddard A crater.

Mare Humorum is located on the nearside of the Moon, at 24.4S and 38.6W. This region has rich geological structures, which not only experienced early lunar volcanic activity but also obtained a mascon. The research on thermal behaviors in Mare Humorum can bring new insights into the multiring impact basins. In this study, Chang’e-2 lunar microwave sounder (CELMS) data were used to obtain brightness temperature (TB) maps. To highlight the characteristics of Mare Humorum, the normalized TB (nTB) was generated. By analyzing the daytime and nighttime nTB maps, we found that there are both hot regions and cold spots in Mare Humorum. Combining Clementine UVVIS data with LRO Diviner data, some intuitive figures were made to find the reasons for these thermal anomalies in the study area. The results showed that TiO2 abundance (TA) and rock abundance (RA) are the factors for TB anomalies in Mare Humorum.

X-ray safety inspection machines are essential for maintaining security. In the past few years, the rapid development and widespread application of deep learning have significantly contributed to the progress of X-ray security detection. In order to achieve automatic detection of dangerous objects in X-ray baggage images, this paper proposes an efficient dangerous objects detection network called X-YOLO, which incorporates feature fusion and attention mechanisms specifically for X-ray baggage images. In the proposed network, an innovative attention mechanism module is integrated into the architecture, and an improved Dynamic Head module is designed to enhance object detection across various dimensions. In the experiments, the HiXray and OPIXray datasets are selected, and the experimental results show that the X-YOLO network demonstrates excellent performance when compared with several state-of-the-art networks. The mAP50 value reached 86.6% on the HiXray dataset and 93.7% on the OPIXray dataset, representing improvements of 2.4% on the HiXray dataset and 4.6% on the OPIXray dataset compared with the baseline.

Underwater images often suffer from significant information loss in the red color channel, resulting in a predominantly bluish or greenish tone. Existing enhancement methods struggle to address this issue due to uniform enhancement applied to the bluish and greenish channels, resulting in overcompensation or under-compensation in the red channel. To address these challenges and achieve a more natural color restoration in underwater images, we propose the adaptive color compensation and enhancement (ACCE) algorithm. The ACCE algorithm comprises several essential steps. Initially, to recover the loss of red channel information more effectively, we divide the images into bluish and greenish components for preliminary color compensation (PCC) in the RGB color space. Subsequently, we introduce a novel minimum color loss (MCL) constraint to regulate the PCC, ensuring balanced histogram distributions across the RGB channels. Furthermore, for improved color balance in the enhanced underwater image, we design the fine-tuning color compensation (FCC) to the$a$and$b$channels of the CIELAB color space. Ultimately, we employ the Contour Bougie (CB) enhancement algorithm to restore contour details in underwater images. Experimental results validate the superiority of the proposed ACCE algorithm over state-of-the-art methods, as demonstrated through qualitative and quantitative comparisons. In addition, ACCE exhibits promising generalization and potential for broader applications, encompassing tasks such as dehazing and lowlight image enhancement.

The Crater Detection (CD) presents challenges due to complex backgrounds, tiny scales, and dense distribution of small craters. To address these problems, we propose a new DEtection TRansformer (DETR) variant network for crater detection called Crater-DETR. First, we design the Correspond Regional Attention Upsample (CRAU) and Pooling (CRAP) operators through cross-attention computing to address the problem of foreground-background confusion caused by the feature loss of small craters. Then, to address the weak supervision due to the fixed number query selection and to introduce dense supervision, we propose the Dense Auxiliary Head Supervise (DAHS) training. Next, Automatic DeNoising (ADN) training is proposed to solve the problem of sparse positive queries in the Decoder to improve the decoding capability. Finally, we propose a Small Object Stable Intersection over Union (SOSIoU) Loss to optimize the training process since the matching process is more unstable in small craters compared to other sizes of craters. Experimental results show that Crater-DETR achieves a precision of 88.13% on DACD dataset, yielding state-of-the-art performance. Furthermore, our method also achieved the best performance on ISDD and AI-TOD datasets, verifying its robustness.

The regional analysis of lunar brightness temperature (TB) data by the microwave radiometer (MRM) onboard the Chang’E-2 (CE-2) lunar probe holds significant importance in the field of thermal characteristics and TB distribution. However, most existing methods rely on a 2-D conversion, which makes it challenging to represent spherical TB data directly due to the nondevelopability of the sphere. Inspired by the triangular domain, we propose a hierarchical spherical algorithm for lunar regional TB analysis, which embeds the spherical triangular domain into the interpolation. First, we construct an octahedral framework based on spherical area coordinates (SACs), which obtain spherical information over the triangular domain by establishing coordinate correlation to enhance the feature representation ability of TB data. Second, we propose a spherical triangulation approach to reduce the loss of detailed TB information in the lunar regions. Finally, the hierarchical spherical algorithm refines the spherical triangular lattices from coarse to fine in the lunar regions. It integrates all the spherical TB functions from each level of the hierarchy into the 3-D features on the sphere, minimizing the result errors. The experimental results suggest that the hierarchical spherical algorithm for lunar regional TB construction has competitive performance and outperforms several representative methods. Extensive experiments are carried out to analyze the diurnal TB variations in typical regions, i.e., the Copernicus, Tycho, Aristarchus, and Stevinus.

With the continuous advancements in the Chinese lunar exploration program, the substantial brightness temperature (TB) data acquired by the Chang’e (CE) orbiter series have provided unprecedented opportunities for studying the geology of the Moon. In particular, the TB data obtained by the CE-2 mission have offered a new observational perspective for investigating the geological characteristics of the Moon. In this article, we propose a novel triangulated spherical technique for constructing a TB model of the Moon with CE-2 microwave radiometer (MRM) data. Specifically, we directly parameterize spherical TB data based on spherical area coordinates and use spherical Bézier surfaces to capture the TB distributions for daytime and nighttime across different frequency channels. This model is enabled by the theoretical characterization of spherical data, which allows us to construct geometric formulations that parameterize the entire TB data of the Moon. It incorporates an octahedral partitioning strategy and divides the lunar surface into eight regions, each undergoing iterative refinement to enhance precision through detailed analysis. Furthermore, the spherical Bézier surfaces effectively mitigate the complexity of structural components on TB estimations. Experimental results demonstrate that the proposed TB model of the Moon significantly outperforms existing approaches in characterizing the lunar TB distribution, providing important application in analyzing the geological features and thermal processes of the Moon.

Land cover mapping is crucial for natural resource assessment, urban planning, and sustainable development. Land cover nomenclature often includes two or three hierarchic levels with tree-like hierarchical structures. This study aims to explore these hierarchical relationships and the potential of hierarchical semantic segmentation for land cover mapping. We propose a hierarchical semantic segmentation architecture by taking advantage of dual U-shape network, named as HierU-Net. The coarse-level result is ingested to the fine-level segmentation functioned as soft constraints. The propagation of error will not be certain. Moreover, we employ a multi-task loss function weighted by homoscedastic uncertainty to optimize the training. To evaluate the performance of the proposed method, we create a hierarchical semantic segmentation dataset (HierToulouse), which contains 11,528 samples, including images and land cover labels at two hierarchical levels. The experiments demonstrate that the proposed approach is capable of achieving accurate land cover segmentation at both coarse and fine levels, with segmentation results surpassing those obtained using the flat method.

Brightness temperature (TB) derived from the Chang’e-2 microwave radiometer (MRM) data has provided a useful way to study the thermal and dielectric properties of the subsurface deposits on the Moon. However, the obvious TB change with the latitude, named latitude effect, has highly limited the application of the MRM data. To solve this problem, a new TB mapping method, named normalized TB (nTB) mapping method, is developed, which is defined as the ratio between the TB and the standard TB at the same point. Based on the newly derived global nTB maps, we identified and classified four types of subsurface deposits with distinct dielectric properties, two of which indicate the abnormally high heat flux or the existence of the granitic systems and the existence of the surface rocks, respectively. Moreover, the nTB at the daytime demonstrates a strong correlation with both the TiO2 and FeO abundances of subsurface deposits, the latter of which has been severely underestimated by the previous studies directly using MRM data. This work is significant to improve the understanding of the basaltic volcanism and thermal evolution of the Moon.

Spaceborne infrared ship detection holds immense research significance in both military and civilian domains. Nonetheless, the focus of research in this field remains primarily on optical and synthetic aperture radar (SAR) images due to the confidentiality and limited accessibility of infrared data. The challenges in spaceborne ship detection arise from the long-distance capture and low signal-to-noise ratio of infrared images, which contribute to false alarm misclassifications. To handle this problem, this article concentrates on enhancing information interaction during feature extraction to discern disparities between targets and backgrounds more effectively, and we propose a multitype feature perception and refined network (MFPRN). Specifically, we propose a dual feature fusion scheme, which combines a fast Fourier (FF) module used to obtain comprehensive receptive field and a lightweight Multilayer Perceptron (MLP) applied to capture the long-range feature dependencies. Besides, we adopt a Cascade region proposal network (RPN) to leverage high-quality region proposals for the prediction head. Through the extraction of rich features and refined candidate boxes, we successfully mitigate false alarms. Experimental results illustrate that our method significantly reduces false alarms for general detectors, culminating in state-of-the-art performance as demonstrated on the public infrared ship detection dataset (ISDD) baseline.

Audio-visual products are an important carrier of ideological communication, which meets the diverse, multiaspect spiritual and cultural needs. However, although current audio representation methods have shown superior performance, they have some limitations and cannot fully take advantage of the diverse, multilevel characteristics of the audio-visual products. The article proposes a complex system-based audio representation method, which diverse audio signals are represented uniformly as Gaussian integers and marked into the same 2-D complex plane. Especially, the relationships of audio signals can be redefined based on the geometric properties. The experiments demonstrate that the proposed audio signal representation method is feasible and lossless. Furthermore, we explore potential industrial information security applications under the proposed representation method, and the applications verify that the representation method has the practical value.

Traditional Chinese medicines (TCMs) play an important role in the treatment of many diseases. For industrial production, classical TCMs identification methods suffer from high labor cost and low efficiency. Moreover the complex multi-object combinations of TCMs lead to serious feature confusion problem. In this article, we propose a novel detection network for TCMs called TCMnet. It focuses on the performance degradation caused by the images in different datasets containing different number of objects. First, an innovative multilevel feature fusion framework is proposed, which improves the generalization of the model. Then, a receptive field controlling architecture is established to limit the receptive field for reducing the confusion among multiple objects. Finally, a trainable feature resolution enhancement algorithm is proposed to increase the precision of classifier by enhancing local detail information. In the experiments, we choose 18 classes with 1800 images from our TCMs dataset. The experimental results show that TCMnet proposed in this article is able to mitigate the feature confusion problem in single-multiple object detection. In addition, TCMnet achieves a good accuracy compared with other detectors on single-object and multi-object detection tasks.

Industrial image processing is a major technology in the fourth industrial revolution. In recent years, the spline method has gained more and more attention in image processing. To explore an efficient industrial image processing algorithm, this article proposes novel cubic polishing spline algorithms. The cubic polishing spline is a high precision spline algorithm that brings more degrees of freedom by adding nodes to better balance the local and global aspects. First, the direct and indirect cubic polishing spline transforms are used for the image interpolation filter. Then, we construct the least squares cubic polishing spline and pyramid approximation method with application to the image reconstruction and image scaling (image upscaling or downscaling). Next, the frequency domain cubic polishing spline lowpass filter and highpass filter are proposed, and the proposed schemes are applied to image smoothing and image sharpening, respectively. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) and root mean square error (RMSE) evaluation metrics are used to illustrate the reconstruction effects, and experiments show that the algorithms proposed in this article can achieve better reconstruction results with better fidelity and competitiveness compared with other schemes.

Underwater detection is a crucial task to lay the foundation for the intelligent marine industry. In contrast to land scenes, targets in degraded underwater environments show ambiguous and surrounding-similar profiles, causing it challenging for generic detectors to accurately extract features. Eliminating the interference of ambiguous features is one of the primary goals when recognizing underwater objects against complex backgrounds. To this aim, we propose a novel detection framework called underwater distraction mining detector (UDMDet). UDMDet is an end-to-end detector and has two key modules: distraction-aware FPN (DAFPN) and task-aligned head (THead). DAFPN is designed to progressively refine the coarse features via mining the discrepancies between objects and backgrounds, while THead enhances the information interaction between classification and localization to make predictions with higher quality. To overcome the feature ambiguous problem, the underwater distraction-aware model is proposed to extract the differences between objects and surroundings so as to clear the target boundary. Experimental results show that UDMDet can more effectively discover objects conceal on real-world underwater images and has a higher precision outperforming the state-of-the-art detectors.

Spline functions have received widespread attention in the fields of image sampling and reconstruction. To enhance the performance of splines in reconstruction and reduce the computational burden of solving large linear equations, we propose a family of generalized cardinal polishing splines (GCP-splines) and provide a system of linear equations to obtain the expressions of GCP-splines. First, we propose a cardinal polishing spline basis function with high-precision. Then, we propose a class of GCP-splines and give a general theory of GCP-splines. To calculate the expressions of GCP-splines, we adopt a system of linear equations to obtain the time shifts operator and the convolutional coefficients based on the search spacing and number of terms. Finally, we propose continuous and discrete interpolation models based on GCP-splines, and demonstrate several valuable properties, such as order of approximation and the Riesz basis. To evaluate the performance of GCP-splines, we conduct several experiments on test images from different modalities. The experimental results demonstrate that the GCP-splines for image interpolation and image denoising have better performance and outperform other methods.

Siamese trackers usually use the target in the first frame as a fixed template, but the static template cannot adapt to target changes. The existing updater is challenging to deal with target deformation and update noise, and there is an excellent risk of updating with an inaccurate updater. In our research, a dynamic template updating strategy based on spatial-temporal information is proposed to improve the tracking accuracy of the Siamese tracker. Furthermore, Tracking Confidence Network (TCNet) is proposed to judge whether to update, which ensures that high-quality target features are used to update and reduce the noise caused by adding unreliable targets. In experiments, the proposed method is embedded into two baseline trackers: SiamRPN and SiamFC++, and tested on five popular benchmarks. The experimental results show that the proposed method can improve the performance of the Siamese trackers while maintaining real-time speed.

Sinus Medii has various geological features, and its location in the central part of the lunar nearside is ideal for calibrating ground-based lunar observation instruments. In this work, the Chang’E-2 (CE-2) microwave radiometer (MRM) data were used to evaluate the microwave thermal properties of the surface deposits in Sinus Medii combined with the previous geological results based on optical data. The findings are as follows: (1) a lunar calibration site (4.98°W, 3.02°N) is found where the TB performances show a good agreement with the compositions of the surface deposits, (2) a TB anomaly (0.24°W, 0.76°N) was found which may be related to high rock abundance in a crater, and (3) a possible cryptomare is revealed in the light plain southeast of Sinus Medii. This study of the microwave thermal properties in Sinus Medii helps search for calibration sites for ground-based observation instruments.

Mare Smythii is a special basin, which is one of the oldest mare basins with the considerably young mare basalts and the high density of floor-fractured craters. In this paper, the Chang’E-2 (CE) lunar microwave radiometer (MRM) data was used to evaluate the thermophysical features of the floor deposits in mare Smythii. Based on the theoretical simulation and the previous geological results, several special findings are noted as follows. (1) The substrate temperature in mare Smythii is likely fairly high. (2) The regolith thermophysical parameters change greatly with depth in the northeast unit, and there probably exists a special material in the shallow layer of the lunar regolith with strong thermal absorption ability. These special findings will be of fundamental significance to improve understanding the thermal evolution of the Moon.

Infrared image has received much attention, but the weak features and multi noise in it bring difficulties to object detection. In this letter, an improved YOLOX called YOLOX-IRI is proposed to improve the detection accuracy on infrared images. First, an improved CBAM is proposed to make the network focus on the object area. This module enriches the feature information by mixing three kinds of pooling methods properly, which helps the network distinguish between background and object. Second, class-balanced loss is introduced to suppress adverse effects caused by unbalanced sample distribution problems. This loss function can balance the contribution of each class to the total loss by assigning weights, thereby improving the classification result. Experimental results indicate that our method is superior to other object detection algorithms.

Chang’E lunar microwave sounder (CELMS) data have great significance for studying the lunar surface’s thermophysical features and internal structure. In this study, an area centered at Glushko crater (8.4°N, 77.6°W), one of the typical Copernican craters on the Moon, is selected as the study area. Its microwave thermal emission features (MTE) were investigated with the CE-2 CELMS data. The main results are as follows. First, Glushko crater is revealed as a cold spot for the first time due to its special brightness temperature (TB) performance. Second, the correlation analysis shows that the high rock abundance plays the most important role in theTBperformance observed at Glushko crater. Moreover, other factors, such as the ilmenite and the surface topography, contribute to theTBdistribution to a certain degree, but they do not possess the decisive influence. Furthermore, the spatial difference of theTBof Glushko crater floor reveals that lunar rocks are probably less abundant in the southern and western parts of Glushko crater.

King crater is a Copernican crater located near the equator in the western highlands of the lunar farside. The study of the microwave brightness temperature (TB) in this region is of great significance for exploring the interior structure of the lunar farside. The Chang’e-2 (CE-2) microwave radiometer (MRM) is mainly used to obtain TB data on the lunar surface under four channels. This study uses the ordinary Kriging algorithm to interpolate the midday and midnight MRM data of this region and generates the TB maps and TB difference (dTB) maps for each channel in two moments. Combined with UVVIS data from Clementine satellite and Diviner data from LRO satellite, the characteristics of microwave thermal radiation are discussed. The results show that (FeO+TiO2) abundance (FTA) and rock abundance (RA) are important factors affecting thermal radiation anomalies in the King crater. It also provides evidence for the formation of the King-Bruno anomalous belt, which is believed to be significantly related to the formation of the King crater.

In view of the current situation of low visibility at night and increasing poaching activities in wildlife reserves, the use of unmanned aerial vehicle (UAV) monitoring equipped with thermal infrared (TIR) cameras has become a trend. In order to overcome the difficulties of small objects and low resolution in infrared images, this letter proposes a non-salient object detection algorithm in infrared images based on deep learning. Based on Grid R-CNN, we designed a feature extraction network, improved the region proposal network by guided anchoring (GA-RPN), and introduced the slice inference mechanism. Our method makes the network selectively fuse multi-scale features to generate high-quality proposals and improve detection accuracy. Experimental results show our method is superior to other object detection algorithms for non-salient elephant images on the BIRDSAI dataset.

Humans gradually learn a sequence of cross-domain tasks and seldom experience catastrophic forgetting. In contrast, deep neural networks achieve good performance only in specific tasks within a single domain. To equip the network with lifelong learning capabilities, we propose a Cross-Domain Lifelong Learning (CDLL) framework that fully explores task similarities. Specifically, we employ a Dual Siamese Network (DSN) to learn the essential similarity features of tasks across different domains. To further understand similarity information across domains, we introduce a Domain-Invariant Feature Enhancement Module (DFEM) to better extract domain-invariant features. Moreover, we propose a Spatial Attention Network (SAN) that assigns different weights to various tasks based on the learned similarity features. Ultimately, to maximize the use of model parameters for learning new tasks, we propose a Structural Sparsity Loss (SSL) that can make the SAN as sparse as possible while ensuring accuracy. Experimental results show that our method effectively reduces catastrophic forgetting compared with state-of-the-art methods when continuously learning multiple tasks across different domains. It is worth noting that the proposed method scarcely forgets old knowledge while consistently enhancing the performance of learned tasks, more closely aligning with human learning.

The Chang’e-1/2 satellites carried microwave radiometers (MRM), which supplied unique passive microwave data, supplementing visible, thermal infrared, and radar data in current lunar studies. However, the application of MRM data is constrained by the limited amount of the original dataset, which cannot express spatial variations at given local times. In this study, we present a novel approach to constructing a local-time brightness temperature (TB) model using barycentric interpolation based on Delaunay tetrahedralization. This model enables the generation of a complete TB dataset, producing TB maps that are both continuous and self-consistent in both spatial and temporal domains. Compared to the traditional methods, those generated in this work avoid a ~7-K bias on a global scale. Furthermore, the interpolated maps offer superior representations of the TB over time for various lunar surfaces. The preliminary evaluations on global and regional scales hint important application of MRM data in studying the geological features of the Moon.

SAR ship detection is sensitive to the interference of inshore background, disturbance of strong wind and waves. The similar textures of the neighbor objects in SAR images affect the detection performance. As a remarkable indicator, textural information in the frequency domain characterizes the subtle textural differences between an object and its surroundings. Inspired by this, a multi-feature fusion network (MFFN) for SAR ship detection is constructed in this paper, which can obtain contour and detail information of a SAR image for detecting ships from their background. Firstly, spatial and frequency information of ship targets, which characterizes the whole and subtle textural information of ship targets, are extracted by a double-backbone network with Haar wavelet transform. Afterward, a binary domain feature pyramid network (BDFPN) with feature fusion block (FFB) is applied to fuse the spatial, frequency textural information of ship targets to obtain the fused feature maps with a top-down structure. Finally, those feature maps are adopted through the region proposal network for detecting ship targets from original images. The experimental results show that the proposed method achieves greater performance and more accurate detection results in unique situations in the state-of-the-art SAR ship detection data set (SSDD).

Thermal infrared (TIR) technology is crucial for wildlife detection in unmanned aerial vehicles (UAVs), allowing executives to explore and detect at night. However, the images captured by TIR cameras are unavoidably affected by various unexpected challenges such as image jitter, wildlife overlap, and fog, which may drastically decrease wildlife detection ability. To overcome these challenges, we propose a high-accuracy infrared object detection method called channel enhancement RetinaNet (CE-RetinaNet). Firstly, a new channel enhancement (CE) module is proposed to strengthen the feature extraction of infrared images. Then, a new batch-norm stochastic channel attention (BSCA) module is proposed to filter occlusion-caused anomalous activations and focus on the pixel in the same position across channels. Next, a path augmentation (PA) operation is added after the feature pyramid network (FPN) to improve the localization capability at the entire feature level. Finally, we modified the output strategy of the classification and regression subnets. Additionally, we built a TIR wildlife detection dataset called the Infrared Salient Object Detection (ISOD) comprising 2534 images, which is accessible by the website: https://doi.org/10.5281/zenodo.7445307. We conduct extensive experiments on both public and ISOD datasets, and the experimental results reveal that CE-RetinaNet obtains higher average precision (AP) (e.g., 11.3% more) and Recall (e.g., 11.6% more) compared to other state-of-the-art object detectors.

Light traveling through water results in strong scattering across color channels, restricting visibility in underwater images. Many cutting-edge underwater image enhancement methods encounter limitations in color recovery accuracy and resilience against irrelevant feature interference. To tackle these degradation challenges, we propose an efficient and fully guided information flow network called UGIF-Net, for enhancing underwater images. Specifically, we propose a multi-color space-guided color estimation module that accurately approximates color information by incorporating features from two color spaces within a unified network. Subsequently, we employ a dense attention block to guide the network in thoroughly extracting color information from both color spaces while adaptively perceiving crucial color information. Moreover, we devise a color-guided map to steer the network’s focus toward color information and augment its response to color quality degradation. We incorporate the guided map into a guide color restoration module to achieve visually appealing enhancement results. Comprehensive experiments indicate that our approach surpasses state-of-the-art methods, showcasing favorable image restoration effects and their potential to aid other high-level vision tasks.

Low contrast, color distortion, and blurred details are common problems that perplex vision-guided underwater robots. To this end, we propose an underwater image enhancement framework via self-attention and contrastive learning (UIESC) to solve these problems. In this article, local features and global dependencies are constructed through space and channel dual attention, and criss-cross attention is used to solve the high computational complexity of self-attention. Moreover, contrastive learning is introduced into network training as a loss function, and contrastive regularization ensures that the enhanced images are closer to clear positive samples and away from source negative samples. Finally, smoothed-histogram equalization is adopted for further optimization to accommodate complex and variable underwater scenes. Extensive experiments have shown that our framework outperforms state-of-the-art methods in underwater image enhancement tasks.

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, which contains sensitive information. Therefore, how to protect data security is an important challenge in the development of IIoT. To this end, we propose a duple color image encryption method for IIoT. Different from the traditional algorithm that encrypts one plain image into one ciphertext image, our algorithm encrypts two color images into one color ciphertext image which can cause great confusion to the attacker who illegally breaks the ciphertext image. First, the proposed algorithm converts two color images with$N\times M$into a 3-D bit-level matrix with$N\times M\times 48$. Next, 3-D nonequilateral Arnold transform (3D-NEAT) is applied to permutate the positions of the elements of the resulted 3-D bit-level matrix. Then, the permutated 3-D bit-level matrix is transformed into three 2-D pixel-level images and then diffused by the random diffusion sequences that 3-D Lorenz system (3D-LS) generates. Finally, the scrambling matrices generated by 3D-LS are used to scramble three diffused 2-D pixel-level images, and the output is considered as three color components of the encrypted image. The numerical experiments and security analyses show that the proposed image encryption scheme has strong resistance to several known attacks, and yields near-zero correlation and near-eight entropy for the RGB cipher image, and its performance is better than some of the recently proposed image encryption algorithms.

In this study, two special thermal behaviors are discovered and identified with the brightness temperature (TB) derived from Chang'e-2 MRM data. First, there exists a 37-GHz TB at noon, which is not related to the topography. Second, there exists a low 3.0-GHz TB anomaly. After comparison the distribution pattern and the correlation coefficients between the rock abundance, or FeO and$\text{TiO}_{2}$abundances, we ascribe the special thermal behaviors to be a composition-related TB anomaly, which likely reflects the heterogeneity of the lunar crust in vertical direction.

Studying Tsiolkovskiy crater with mare basalt in the floor helps to better understand the volcanic evolution and geological events on the lunar far side. In this work, the thermal behaviors of the surface materials in Tsiolkovskiy are evlated with the microwave radiometer data from Chang'e-2 satellite. The main results are as follows. (1) The thermal behaviors in the central peak coincide with those of rocks. (2) A small patch with higher substrate temperature is discovered in the southwestern part of crater floor with basaltic material. (3) The difference between the eastern and western half of Tsiolkovskiy crater is pointed out according to the brightness temperature behaviors.

Studying Korolev basin may provide meaningful information about the Megabasin on the lunar surface. In this study, the brightness temperature (TB) maps derived with Chang'e-2 microwave radiometer data are used to study the thermophysical features of the surface materials within and outside Korolev basin. Here, for the first time, a high TB anomaly in the highland crater is revealed with the TB data. Moreover, four surface units are indicated by the TB performances at day and night, which likely reflects the thermophysical features of the ejecta mainly from Orientale, Hertzsprung, and Apollo events. This study is essential to improve understanding the surface and thermal evolution of the highland craters.

Vision-guided autonomous underwater vehicles based on remote sensing play an important role in ocean missions. However, some problems exist in underwater visual perception, such as color distortion, low contrast, and fuzzy details, which restrict the applications of underwater visual tasks. Most of the state-of-the-art image enhancement methods are still limited in scene adaptability, recovery accuracy, and real-time processing. To solve these problems, we propose an underwater sensing scene image enhancement method called a multiscale feature fusion network (MFFN). To extract the multiscale feature, the measure merging the feature extraction module, the feature fusion module, and the attention reconstruction module is designed. This measure can also enhance the adaptability and visual effect of the scene. Moreover, we propose multiple objective functions for supervised training to match the nonlinear mapping. Based on the qualitative and quantitative evaluations, the proposed method produces competitive performance compared with some state-of-the-art methods, and the perception and statistical quality of underwater images are enhanced effectively.

The influence of rocks on the microwave thermal emission (MTE) of the lunar regolith has not been fully studied with the four-channel microwave radiometer (MRM) data onboard Chang’e-1/2 satellites. To highlight the influence of the rocks on the MTE of the regolith, the Hertzsprung basin located near the lunar equator in highland regions is selected as the study area. The comparison between the brightness temperature (TB) maps derived from the Chang’e-2 MRM data and rock abundance (RA) map derived from the Diviner data postulates three special issues about the correlation between the MTE features and the regolith with rocks. Then, aimed to interpret the issues, two new layered regolith models and the corresponding radiative transfer models are constructed. The main results are as follows. First, the observation and the simulation both verify that the regolith with rocks will provide a cold TB anomaly at night and at low frequencies at daytime, but result in a hot anomaly at high-frequency at daytime. Moreover, the temperature profiles of the regolith with surface and hidden rocks are evaluated with the theoretical model. Second, the simulation results verify the existence of the hidden rocks in the lunar regolith assumed when studying the TB performances of the Hertzsprung basin. Third, the rock distribution revealed by the TB maps shows a different view compared to that estimated by the Diviner data in space and values, and the change of the TB with frequencies postulates a new view about the variation of the RA with depth. This study hints that the MRM data probably provide a new way to quantitatively estimate the RA values of the lunar regolith, and the results will be meaningful to improve understanding of the evolution of the impact craters.

Crater detection is widely used in terrain relative navigation, which can help scientists target a spacecraft’s position and estimate the age of a planet. However, high-performance crater detection still remains a very challenging problem because of the complex data distribution, especially when there are lots of small craters. To address these issues, we present an end-to-end deep convolution neural network called high-resolution feature pyramid network (HRFPNet) to well detect impact craters. In the network, a new adaptive anchor calculation and label assignment (AACLA) algorithm is designed to solve the problem of “anchor-sensitive” small craters that cannot be balanced sampled for training, which affects the detection results of small craters. Then, a new backbone with feature aggregation module is presented to enhance the feature extraction capabilities and to preserve the features of small craters in deep neural network, and a new regression loss function called balanced regression loss (BRL) is also applied for the coordinate regression of small craters, which avoid the inaccurate size prediction of small objects due to the large difference of the regression loss value between the small craters and other craters. In addition, we build a crater detection dataset called the Mars day crater detection (MDCD) dataset that contains 500 images with 12 000 craters, which can be downloaded from the website:http://doi.org/10.5281/zenodo.4750929/. We conduct extensive experiments based on the public and MDCD datasets, and the results show that the proposed network achieves the state-of-the-art results; especially, it achieves high performance in detecting small craters.

Existing crater detection methods are typically carried out in the same domain where the training and testing data are drawn from an identical distribution. However, this means that when we apply a trained crater detector to a new scene probably caused by changes in sensors, lighting or other factors will lead to a significant performance drop. In this work, we aim to improve the accuracy of the domain (scene) adaptive crater detection which will reduce labor costs caused by annotation. Firstly, we proposed a progressive domain adaptive network (PDAN) which can progressively learn the knowledge from the source domain to the target domain by the projected intermediate domain features generated by the subspace along the geodesic on the Grassmann manifold. Secondly, in order to further extract the domain agnostic feature and utilize the characteristics of craters, we proposed a low-level feature enhancement module (LFEM) and a circular boundary enhancement module (CBEM). Thirdly, a weighted instance-level alignment network (WIAN) is presented to adaptively align the instance level feature further reduce the differences between two domains, and enhance the performance. To evaluate the effectiveness of the proposed method, based on our previous Mars day crater dataset (MDCD), we present a domain adaptive crater detection (DACD) dataset which contains 1000 images in two domains and more than 20000 craters. The extensive experiments based on the DACD and open datasets demonstrated that the proposed network could effectively eliminate the impact of domain shift and achieve state-of-the-art results.

Real-world underwater images suffer from quality degeneration caused by the scattering and absorption of light propagation. The damage of the detailed textures in underwater images shows the negative effect of detection and recognition. To recovery the image visibility and sharpness for the above applications, a new image enhancement method is proposed for extracting the image textures. To enhance the image textures with high quality, we propose a multiscale fusion enhancement. Two new fusion inputs are built on different color methods. One input is devoted to improve the sharpness by contrast-based dark channel prior dehazing in the red–green–blue (RGB) model. The other input is designed based on multiple morphological operation and color compensation from the opponent color in the CIE$1976~L^{\ast}a^{\ast}b^{\ast}$color space (CIELAB) model. This input is used to enhance the counter brightness and adjust the color distribution. The dominant features of the two inputs are merged. Therefore, the contrast of the fusion output is enhanced adaptively to recover the final enhanced result. Compared with the state-of-the-art methods, our results reveal that the proposed method can enrich the image textures based on an impressive visual perception of contrast, saturation, and sharpness. Moreover, our method also shows strong robustness in challenge scenes and improves the performance of several underwater applications.

With the rapid development of the Industrial Internet of Things, the social and commercial value of digital information is greatly increased, and the sensitive information of groups and individuals faces more and more problems. Due to providing accurate prediction errors, pixel value ordering (PVO)-based reversible data hiding is an active research topic. In this article, a new PVO-based embedding method is proposed first, in which the adaptive data embedding is adopted according to the difference between the maximum/minimum three pixels. Second, a pixel collecting and sorting model is designed to increase expandable prediction errors. By converting a shifting prediction error into an expandable one, the proposed PVO-based embedding method creates conditions for data embedding. In this way, the space location of pixels in the block can be better utilized to achieve improved capacity-distortion performance. Finally, an adaptive embedding is designed based on the content of the target block and its neighboring blocks. By measuring the complexity of the target pixel block, smoothing blocks are preferentially used and the appropriate embedding is determined by the complexity. The experimental results show that the proposed method outperforms a series of PVO-based multipass embedding methods. The proposed method is expected to address some security issues in the industrial and privacy fields, such as enhancing confidentiality and security during the transmission of sensitive information.

In the information age, image processing technologies play a vital role in the field of industrial engineering. In this article, a novel spline scheme called the hierarchical polishing splines algorithm is proposed, and it is applied to the field of computed tomography (CT) image reconstruction of coronavirus disease 2019 (COVID-19). The proposed algorithm defines a set of control lattices, wherein the density of lattice points in these control lattice ranges from coarse to fine in order, and the final applied function is produced by adding the functions derived from every control lattices. In order to demonstrate the performance of the proposed algorithm, some CT images from COVID-19 patients are selected. The experimental results show that the reconstructed COVID-19 CT images by using the proposed algorithm have good quality when compared with some widely used approaches. In addition, this article also applies the proposed algorithm to reconstruct the infected regions derived from COVID-19 CT images, and the results also show that the proposed algorithm is more efficient than others. Besides, the applicability of the proposed algorithm is discussed, wherein the COVID-19 severity is estimated based on the reconstructed COVID-19 CT images, and the applicability analysis shows that the use of the proposed algorithm to reconstruct COVID-19 CT images can help to achieve more accurate severity assessment of COVID-19 in a certain extent.

The rapid development of the Industrial Internet of Things and artificial intelligence has brought new pressures to the compression and storage of image information. Thus, efficient image compression approaches are important in the fourth industrial revolution (Industry 4.0), which can save storage resources, accelerate transmission speed, and improve imaging fidelity. In this article, a new approach of image compression based on Haar-V functions (ICHV) is investigated, which has good performance in industrial image compression. The Haar-V functions form a discontinuous orthogonal function system composed of piecewise polynomials. The construction and properties of Haar-V functions are introduced at first. Then, 2-D discrete orthogonal matrices of Haar-V functions and the corresponding image transform method are presented successfully. Finally, a fast computation of ICHV is proposed to enhance the computation speed of image compression. The experimental results indicate that compared with related algorithms, the new approach can effectively improve the quality of compressed images and consume less computational cost.

As the coronavirus disease 2019 (COVID-19) spreads around the world, industrial automated medical diagnosis systems have been developed, which complete a large amount of medical diagnosis work through computed tomography (CT) images. In these systems, how to quickly store and transmit such a large amount of CT image information has important research significance. In this article, a more targeted COVID-19 chest CT image codec is proposed to make image data not only occupy less space but also have higher image quality. First, the bilateral lung contours are extracted to calculate the position information of the region of interest (ROI). Then, a CT image is classified into four types of nonuniform image blocks according to the characteristics of COVID-19 chest CT images and ROI position information. Next, a series of new transformations are proposed for more efficient transform coding. Finally, a flexible quantization strategy is proposed for the adaptive quantization part. In the experiments, the proposed method is superior to some of the existing methods with similar computational complexity. At the same bit rate, it significantly improves the image quality. This means that chest CT images can still be used for disease diagnosis while taking up less space. In addition, because of the low computational complexity of the proposed method, it can be more easily embedded into the CT equipment with low computational power.

The cold anomaly represents the special thermophysical features of the lunar regolith, which is only studied with the LRO Diviner thermal infrared (TIR) data. In this paper, sampled a typical TIR cold anomaly, the China Chang' E-2 lunar microwave radiometer data are employed to evaluate the regolith thermophysical features in microwave domain. The results indicate that the special material only exists in the shallow layer, and the thickness is larger than 31 cm but less than 77 cm. Moreover, the special material presents a high correlation with the rocks in the microwave domain, opposite to the findings from Diviner TIR data.

The cause of the hot anomaly is one of the important scientific aims of the current lunar studies. In this study, aimed at a rock-related hot anomaly revealed by the Diviner thermal infrared data, the brightness temperature (TB) derived from the Chang'e-2 microwave sounder (CELMS) data is employed to evaluate its thermophysical features of the regolith. According to the TB performances, the ilmenite-rich material is identified in the region. Thus, we denied the rock influence and ascribed the region as a potential cryptomare. This study provides a new way to evaluate the thermophysical features of the lunar regolith with the CELMS data.

The lunar surface has complex geomorphic characteristics. Since the lunar terrain entropy can reflect the amount of geomorphic information contained in the lunar terrain, this article uses the ratio of the elevation value of a local point on the lunar surface to the total elevation value of the neighborhood to calculate the local terrain entropy value of the Moon. Then, the hierarchical polishing splines algorithm is proposed to construct the digital terrain entropy model (DTEM) of the Moon, wherein the new algorithm produces a sequence of functions based on a hierarchy of coarse-to-fine control lattices to generate the modeling function, which has good modeling performance. Using the proposed algorithm, multiscale DTEMs of the Moon are constructed based on square moving windows with different sizes. From the lunar DTEMs, it can be found that the lunar terrain entropy is sensitive to the size of the square moving window and the resolution of lunar DEM, and the high-resolution lunar DTEM with suitable moving window can well show topographical variations. In addition, the lunar terrain entropy distribution models are created based on the lunar DTEMs, which is significantly important to the study of the lunar terrain entropy distribution law. Besides, two terrain parameters, i.e., surface roughness and surface slope, are selected to show that the geomorphic characteristics of the Moon can be well reflected by the lunar terrain entropy.

Underwater images require further enhancement to improve the image qualities caused by medium scattering and light absorption. Based on Contour Bougie (CB) morphology, we propose a new enhancement method to enhance the scene contours and improve the visibility of images captured underwater. Two structuring elements with different sizes are considered as the roving windows. Multiple morphological operations are designed for highlighting the rich details on the origin images. The enhanced images are normalized and stretched to improve the white balance of RGB channels. The comprehensive study of state-of-the-art algorithms is conducted to interpret the improvement of image quality by the proposed method. In addition, we use 890 raw underwater degraded images as the testing data. The quantitative and qualitative evaluations of these data demonstrate that the proposed method achieves better visible contrast for highlighting the details of the undersea creatures. The comparison with different underwater scenes proves that the proposed method improves the color balances of the degraded images.

In the context of the fourth industrial revolution (also referred to as Industry 4.0), industrial image processing forms a valuable basis for newly conceived applications and services, especially image reconstruction as one of the key technologies of industrial image processing plays an important role in industrial engineering. To develop an effective method for industrial image reconstruction, this article proposes the optimized hierarchical many-knot (HMK) splines (abbreviated as OHMK splines) method. In the scheme, a fine merged control lattice is derived from the hierarchy of coarse-to-fine control lattices, and then the final reconstruction function obtained by OHMK splines is the function generated on the merged fine control lattice, which is not obtained by superimposing all functions generated on the coarse-to-fine control lattices. Therefore, the computation for the final reconstruction function relies on the number of control points in the merged fine control lattice, not depending on all the control points in the hierarchy. Through the experimental results, we can find that the good reconstruction performance for the given image can be obtained by using the OHMK splines method, and the computational overhead of the OHMK splines method is superior to that of the HMK splines method.

Digital image feature recognition is significant to industrial information applications, such as bioengineering, medical diagnosis, and machinery industry. In order to supply an effective and reasonable technology of the severity assessment mission of coronavirus disease (COVID-19), in this article, we propose a new method that identifies rich features of lung infections from a chest computed tomography (CT) image, and then assesses the severity of COVID-19 based on the extracted features. First, in a chest CT image, the lung contours are corrected for the segmentation of bilateral lungs. Then, the lung contours and areas are obtained from the lung regions. Next, the coarseness, contrast, roughness, and entropy texture features are extracted to confirm the COVID-19 infected regions, and then the lesion contours are extracted from the infected regions. Finally, the texture features and V-descriptors are fused as an assessment descriptor for the COVID-19 severity estimation. In the experiments, we show the feature extraction and lung lesion segmentation results based on some typical COVID-19 infected CT images. In the lesion contour reconstruction experiments, the performance of V-descriptors is compared with some different methods, and various feature scores indicate that the proposed assessment descriptor reflects the infected ratio and the density feature of the lesions well, which can estimate the severity of COVID-19 infection more accurately.

Reversible data hiding generally exploits the redundancy of the cover medium and prediction-error expansion (PEE) has become the most effective mechanism. However, although the pairwise PEE technique has been proposed to jointly modify the prediction-errors to achieve less degradation, there is still room for improvement. In this paper, a dual pairwise PEE strategy is proposed to fully exploit the potential of pairwise PEE. The key observation behind dual pairwise PEE lies in that most capacity is provided by individually expanding only one pairing error. For such separable error-pairs, we propose to recalculate and collect the rest pairing error to form an error sequence after shifting any one pairing error. Next, by considering every two neighboring errors of the sequence together, a new set of error-pairs for double pairwise PEE can be obtained. Compared with original pairwise PEE, dual pairwise PEE significantly better exploits the correlation of errors such that it leads to better capacity-distortion performance. Experimental results also demonstrate that the proposed scheme outperforms several state-of-the-art schemes.

In reversible watermarking for image authentication, less degradation of the marked image is always desirable. For minimum distortion, the pairwise prediction-error expansion (PEE) technique was recently proposed to modify errors jointly. Although its superiority over conventional PEE has been verified, its potential has not been fully exploited yet. In this paper, we focus on optimal modification and propose an enhanced pairwise PEE. First, it is observed in PVO-based pairwise PEE that the histogram peak varies with relative location when predicting the largest/smallest two pixels. Then, a more effective 2D mapping is proposed by content-dependently selecting the expansion bin after introducing spatial location into prediction. Next, the 2D mapping is further extended considering prediction in non-smooth region tends to produce errors with large magnitude. Finally, we also propose to flexibly define the spatial location to achieve content-dependent prediction and further enhancement. Experimental results demonstrate that the proposed scheme achieves better capacity-distortion trade-off and outperforms several state-of-the-art schemes.

The recognition of vehicle license plate is an important part of the modern intelligent traffic management system, which has been widely used in many fields. On the Hong Kong-Zhuhai-Macao Bridge, the vehicles may have multiple license plates (LPs) with three different styles, and the traditional contour-based vehicle license plate recognition methods cause a considerable miss rate for multi-style license plates. With such a background, this paper proposes a multi-style license plate recognition method based on feature pyramid network with instance segmentation, which translates the license plate recognition into object instance detection and gets rid of the steps of segmentation and optical character recognition of traditional methods. In the scheme, we design a novel license plate recognition network to precisely locate and classify characters and LP regions concurrently, wherein an assembly layer is added for combining the characters into license plates and outputting license plate strings. The experimental results show that the proposed method achieves 98.57% recognition rate of multi-style LPs on the real world applications. Moreover, we also select the standard license plate datasets, that only contain single style license plates, to test the proposed license plate recognition method, and the corresponding results show that the proposed method achieves competitive performance.

Image representation is an active research area in the field of image processing. This paper proposes a novel image representation method under a non-standard positional numeral system, wherein complex numbers are used as bases in such non-standard positional numeral system. It is different with binary code and decimal code, where the digit 2 is used as the base in the binary system, and the digit 10 is used as the base in the decimal system. In the proposed image representation method, a two-dimensional image is transformed into a one-dimensional 0$\sim$1 sequence, and its Gaussian integer is calculated based on the derived one-dimensional 0$\sim$1 sequence. On the contrary, the original two-dimensional image can be recovered from its Gaussian integer. When images are represented as Gaussian integers, the classical geometrical operations are introduced into image processing. Then, the relationship of different images is established by the methods of plane geometry, i.e., the addition, subtraction, multiplication, division, conjugate, and inverse operations of images are defined. The experimental results show that the selection of complex number as base in the positional numeral system is a very special coding method, a given digital image is effectively converted to a Gaussian integer by using the proposed image representation method, and the image arithmetic is also successfully achieved. In addition, three applications based on the proposed image representation method including image camouflage, image sharing, and image scrambling are selected to demonstrate that the new image representation has good and potential practical applicability in the field of secure encryption of digital images.

The cover image is generally encrypted by a stream cipher in existing reversible data hiding in encrypted image (RDHEI) methods. As pixel correlation is seriously damaged, more than one pixel should be employed to carry one bit such that the quite limited capacity is achieved. To overcome this issue, a new RDHEI method with high capacity, that preserves pixel correlation and exploits it to vacate embedding room, is proposed in this paper. First, we propose a block-level encryption scheme which combines block-level stream cipher and block-level permutation, and all blocks are classified into usable blocks (UBs) and unusable blocks (NUBs) by preserving the correlation of pixels in blocks. Then, UB is reconstructed to vacate room for data embedding, because the pixels in blocks share the same most significant bits (MSBs). To ensure reversibility, the number of NUBs between current UB and the previous one is also embedded along with additional data, and the blocks are rearranged in a reversible way such that UBs are always in front of NUBs. Experimental results show that not only the embedding capacity is significantly improved but also the hidden data can be losslessly extracted, and the cover image can be perfectly recovered.

Since digital images are able to be encoded through the luminance-bandwidth-chrominance (YUV) mode, and the contribution of luminance information is greater than that of chrominance information for human visual perception, it can be inferred that the appropriate reduction of chrominance information in convolutional neural network does not disturb image object recognition. In this paper, we propose a new multi-scale non-uniform convolution called YuvConv, wherein the output feature map of the convolutional layer is regarded as an image. First, the output channels in the new convolution are divided into three kinds of components: Y, U, and V tensors. Then, the tensor Y is used to process luminance information, which is high-resolution and occupies more output channels. Next, the tensors U and V are low-resolution and use fewer channels to process chrominance information. Finally, the adjacent tensors (Y-U, Y-U-V, and U-V) are fused as the output of YuvConv. Experimental results indicate that the use of the YuvConv instead of the standard convolution can improve the performance of deep learning tasks, and it can also reduce memory consumption and computation cost.

JPEG lossy image compression is a still image compression algorithm model that is currently widely used in major network media. However, it is unsatisfactory in the quality of compressed images at low bit rates. The objective of this paper is to improve the quality of compressed images and suppress blocking artifacts by improving the JPEG image compression model at low bit rates. First, the image texture adaptive non-uniform rectangular partition (ITANRP) algorithm is proposed which partitions the image into$8\times 8$size image blocks with high texture complexity and$16\times 16$size image blocks with low texture complexity. Then, a new transform coding based on the complete orthogonal U-system and all-phase digital filter (APDF) is proposed for coding image blocks with different sizes. Next, a flexible adaptive quantization scheme is designed to quantize image blocks with different sizes by considering the sensitivity of the human visual system (HVS) to different texture complexities. Finally, combining the above method with the JPEG model, a novel image compression algorithm model with low algorithm complexity is proposed to solve the problem in JPEG. The experimental results demonstrate that the performance of our algorithm model outperforms the JPEG image compression algorithms, the quality of the compressed image is greatly improved, and the blocking artifacts are also significantly suppressed.

Mare Nubium is one of the most ancient circular impact basins on the Moon with the lava flow units ranging from Imbrian to Eratosthenian. In this paper, the China Chang'E-2 lunar microwave radiometer data are employed to evaluate the basaltic units of the Mare Nubium. The results indicate that the relationship between the brightness temperature difference (dTB) performances and the basaltic units is rather weak. Moreover, the low dTB anomaly in the middle-western part is interpreted as the floor deposits produced during the formation of Mare Nubium.

Monotonic curvature plays an important role in industrial design and styling of curves with aesthetic shapes, e.g., in automobile and aircraft design [1].Used in conventional parametric CAD/CAM systems, general B-splines are not adequate for aesthetic requirements.Except for the straight line and circle, monotonic curvature distribution, associated with pleasing shape, is very difficult to achieve.So Farin suggested that a fair curve has a curvature plot with relatively few regions of monotonically varying curvature.Starting from this basis, work on B-spline fairing was developed mainly in three direction: knot-removal-reinsertion methods, optimization methods based on minimizing an energy function, and filtering approaches based on B-spline wavelets.Visual curve completion (interpolating a curve segment, with continuity, to fill a gap) is a fundamental problem for human visual understanding [2].Aesthetically pleasingly shaped curves usually have monotonically varying curvature [3].While the shape of a curve is primarily defined by its curvature distribution, monotonicity of curvature is not easily achieved and controlled.To overcome this problem,

As the core of prediction-error expansion technique, prediction method has a fundamental impact on performance of reversible data hiding. Pixel value ordering (PVO) prediction has been extensively investigated for its high accuracy. However, the correlation of pixels within block has not been fully exploited yet. In this paper, a novel prediction method is proposed by developing PVO prediction in the aspects of spatial correlation and correlated pixel pair. The key to PVO embedding is invariant pixel value order such that the predicted pixel can be identified by value. Instead of predicting and enlarging the largest pixel, we propose to predict and reduce the second largest one and even all others. As the largest pixel which serves as predicted value is maintained after embedding, the numerous predicted pixels can be identified and thus reversibility is guaranteed. IPVO prediction which location-dependently determines the predicted pixel is also developed. For further optimization, multi-pass IPVO embedding is extended from single-layered to double-layered such that full-enclosing pixels can be used to estimate pixel distribution and determine the optimal mode of defining spatial location. Finally, an enhanced pairwise PEE is incorporated with multi-pass IPVO for performance enhancement. Experimental results show that the proposed scheme not only outperforms PVO embedding and its miscellaneous extensions, but also achieves significant superiority in fidelity over a series of state-of-the-art schemes.

The partition algorithm as a digital image processing technique is significant to many applications, such as data encryption, image denoising, and 3-D reconstruction. In order to achieve well partition that can availably reduce the distortion phenomenon, a novel approach named image adaptive triangular partition (IATP) is proposed, which considers the grayscale distribution of the image and removes the shared edges between the adjacent triangles in the partition mesh. The least-squares method is used to fit the sampled position-associated gray value of the image to determine whether further partition should be performed, that is, if the sum of squared residuals is bigger than the preselected control value, the current area will be divided into four separated sub-triangles by using the self-similar method, and then preparing the next fitting on each of them in recursion; otherwise, the terminal operation is reached. When the recursive partition of the image is done, the triangular partition mesh with the quaternary notations is obtained. The experimental results demonstrate that the performance of the IATP algorithm proposed in this paper is better than the existing state-of-the-art nonuniform partitions, and it solves the redundant coding problem and reduces the image quality losses. In addition, two applications-image steganography and information encryption-are selected to verify that the proposed algorithm has good feasibility and robustness.

Apollo Basin locates within the large South Pole-Aitken Basin (SPA). The study on Apollo Basin will provides some interesting information about the composition and thermal state of the shallow Moon crust. In this paper, the normalized brightness temperature (nTB) maps and the (FeO + TiO2) abundance (FTA) were systematically combined to study the thermal behaviors of Apollo Basin. The results firstly indicate a strong correlation between the nTB behaviors and the FTA. Secondly, the low nTB behaviors hint the homogeneity of the Moon crust in the thermophysical parameters. Finally, the abnormally high nTB behaviors in the western of the Basin floor probably imply the existence of the pyroclastic deposits.

Knowledge of the thermal state will provide essential information to better understand the thermal evolution of the Moon. In this paper, four typical Copernican craters, including Copernicus, Aristarchus, Tycho and Jackson, are selected and their thermal behaviors are evaluated with the CE-2 CELMS data. The results indicate that: (1) There exists a strong correlation between the TBdistribution at noon and the topography. (2) The changes of the regolith thermophysical parameters with depth are rather complex in the four typical craters. (3) The TBat midnight is more suitable to study the regolith thermophysical features. (4) The shallow layer of the lunar crust is likely cold.

Surface roughness is a land-surface parameter that is widely used in terrain analysis. Some typical roughness details, which have important effects on surface analysis, fail to be characterized on previous roughness maps. The objective of this paper is to provide a more accurate small-to-large scale roughness overview. The new roughness method is designed based on a complete orthogonal system called the V-system. The V-system roughness utilizes the special functions to detect and extract the roughness characteristics from high-resolution digital elevation models (DEMs). In this paper, Lunar Orbiter Laser Altimeter-derived DEMs are used as the source data for the roughness calculation. Compared with the global root-mean-square slope and Fourier-based roughness maps, the V-system roughness maps show that more typical roughness details have been added to clearly indicate the small roughness variations on the large map. Furthermore, the reliability and practicability of V-system roughness are demonstrated based on the multiresolution DEMs. As an example, the statistical parameters of the roughness characteristics in the lunar Maria and highlands identify the fact that the highlands are rougher at all scales than the Maria. And this difference corresponds to the basic roughness property.

The microwave radiometer (MRM) on-board the Chinese Chang'e-2 (CE-2) lunar probe measures the lunar brightness temperature (also referred to as TB) data that are large-scale scientific data. In order to construct lunar TB map, the optimized hierarchical MK splines method is proposed, which uses a hierarchy of coarse-to-fine control lattices to generate a fine control lattice. The computation of the TB construction function is limited to the small number of control points in the merged control lattice, and then the desired high-resolution TB maps are constructed. At the same time, some basis relations between the lunar TB and frequencies are also analyzed based on the constructed TB maps. It can be found that the high-frequency TB map shows lunar topographic features with close similarity. Furthermore, to express the TB distribution features quantitatively, the lunar TB distribution models, including the global TB model of the Moon, the TB model of the lunar far side, and the TB model of the lunar near side, are established based on the constructed TB maps, and the obtained TB distribution models are log-normal distributions. The establishment of the lunar TB distribution model is important to reasonably select the color layer and intensity of color for the lunar TB maps, and is helpful for studying the lunar TB distribution law. In addition, the topographic data measured by the lunar orbiter laser altimeter are selected to discuss the influence of elevation on the lunar TB, and the CE-2 TB data combined with the FeO and TiO2abundances are used to study the microwave thermal emission features of the lunar regolith. The research results have important implications for studying the thermal radiation of the Moon.

The brightness temperature (TB) data of the Moon acquired by the microwave radiometer (MRM) on-board the Chinese Chang'e-2 (CE-2) lunar probe are valuable and comprehensive data, which can be helpful in studying the physical properties of the lunar regolith, such as thickness, physical temperature, and dielectric constant. To construct the accurate and high-resolution lunar TB model with the TB data obtained by the MRM on-board CE-2, 2401 tracks of the original TB data are quantized by using the hour angle processing, and the hierarchical MK splines function (HMKSF) method is presented, which uses a hierarchy of coarse-to-fine control lattices to generate a sequence of TB model functions. The TB model constructor is the sum of the TB model functions derived at each level of the hierarchy. In addition, the lunar TB models with a resolution of 0.5°×0.5° in all four frequency channels are constructed for both the daytime and the nighttime. The obtained models show rich information, e.g., the global distribution of TB over the lunar surface, the effect of frequency on the TB model.

In the era of Big Data, it is very important to study large-scale data fitting methods. In order to ensure the calculation speed and accuracy, we propose a new kind of hierarchical many-knot splines (hereinafter called “hierarchical MK splines,” generally abbreviated as HMK splines) in this paper. The HMK splines method produces a sequence of MK spline functions. These MK spline functions are constructed into one ideal interpolation function by the MK spline refinement. In the case of regular sampling data, HMK splines can achieve the purpose of accurate approximation for the given data points without solving systems of equations. Further, in order to deal with the issues of scattered data fitting, the use of least-squares method will lead to the necessary of solving a linear system of equations. Since the ill-conditioned systems of equations often lead to unacceptable deviation of calculation results, one tries to avoid it as much as possible. The HMK splines algorithm can meet this requirement; it can avoid the intolerable deviation caused by solving systems of equations. Experimental results show that large-scale scattered data fitting can be easily achieved by the HMK splines algorithm and the reconstruction of nonuniform samples has a high accuracy.

Tycho crater is the most prominent crater of Copernican era. In this paper, the spatial and temporal features of the microwave thermal emission (MTE) at Tycho area are studied with the microwave sounder (CELMS) data combined with TiO2abundance, surface slope and roughness, rock abundance data of lunar regolith. The results indicate that MTE at the south part of Tycho crater (Region A) is strongly affected by the surface temperature, and the temperature difference between the exterior and interior of the lunar regolith layer is fairly large. While MTE at the north part of Tycho crater floor (Region B) is weakly affected by the surface temperature, and the temperature difference between the exterior and interior is rather small. Moreover, the vertical structure of the lunar regolith at the east part of Region B is different from that at the west. Furthermore, the radiation here reaches the thermal equilibrium state after 3 A.M. in the morning, which is of essential significance to study the thermal structure characteristic of the Moon. The lunar regolith at the ejecta blanket is similar to that at Region B, while its thermal storage capacity is much higher than that of other places. The correlation analysis presents that the surface roughness and rock abundance have the largest impact on the MTE, followed by surface slope and TiO2abundance.

As Mare Orientale is the youngest and best preserved multiring impact basin on the Moon, it is of essential importance to study its composition and structure for current Moon research. In this paper, the CELMS data from Chang'E-2 satellite are employed to reveal the microwave thermal emission features of the Mare Orientale. The results indicate that the regions with high TBand high TBdifference are strongly influenced by FTA, but FTA is not the only factor. Moreover, the lunar regolith structure is not even at the mare basin. Furthermore, there exist abundant abnormal TBand high TBdifference regions which could hardly be explained by the current Moon research.

A new set of orthogonal moment functions named as GF moments (GFMs) was proposed in this paper. The kernel functions of GFMs is GF-system, which is a class of complete orthogonal spline function set of degree k(k=0,1,2,?). The implementation of GFMs does not involve any numerical approximation and has a rather low computation complexity, since the basis set has the advantages of lower order. These properties make GFMs superior to the traditional polynomial moments such as Legendre moments and Zernike moments, in terms of the image reconstruction. Our simulation results also show that GFMs have a better feature representation capability.

Engineering graph plays an important role in design and manufacture, such as architecture, machinery, manufacture, military and so on. However, almost no persons consider the security and copyright of two-dimensional engineering graph. A novel method for two-dimensional engineering graph watermark based U system is proposed in this paper. Watermarks generated by this technique can be successfully extracted even after rotated, translated, and scaling transformed.