The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

Dark optical flow estimation aims to predict pixel-wise displacement between consecutive noisy dark frames. Existing methods primarily focus on enhancing feature-specific representations before cross-image matching, with few attention devoted to the inherent dark degradation during flow decoding for achieving holistic motion understanding of a given dark scene. In this paper, we introduce the Position-surpassing Flow Estimator (PsFE), which integrates a global graph method into flow decoders to accentuate holistic motion discrimination and robustness. In detail, we incorporate a graph-based motion reconstruction into the decoding paradigm to adaptive aggregate motion-rich feature channels and suppress degraded ones from a more global view. This characteristic suppression retains the graph structure, which is a robust characteristic in the dark. To accurately encode long-range pixel connections, PsFE employs a novel masked global encoder to capture top-kimportant features by using a sparse masking strategy and dynamic inductive modulation that suppresses noise and interference that only exist under dark conditions. Experiments on challenging FCDN and VBOF benchmarks demonstrate the effectiveness of our PsFE with superior performance over advanced methods.

Due to the loss of 3D information, accurate and robust 2D image feature matching remains challenging for many computer vision applications. This paper introduces a 2.5D feature that uses the disparity value from the light field Fourier disparity layer (FDL) as a rough proxy of scene depth. Without explicit depth estimation, a parameterized depth-degraded projection is proposed to construct the geometric transformation of paired features between two light fields. Then, we propose a parameterized learning solution to calculate the depth-degraded projection. This solution estimates a global constant fundamental matrix, a variable disparity-guided translation vector, and a depth compensation term using a very simple network. Although the 0.5D relative disparity provided by the FDL does not represent precise depth, it can also significantly reduce the depth ambiguity in feature matching. Therefore, the proposed solution achieves accurate feature-matching results by minimizing the sum of reprojection errors across all matching candidates. On the public light field feature-matching dataset, the proposed solution outperforms existing 2D image feature-matching solutions and light field feature-matching algorithms in terms of matching accuracy and robustness. The code is available online.

The space-air-ground integrated network has emerged as a critical enabler to achieve high-capacity 6 G communications. However, frequent handovers between satellites and gateways, along with unbalanced gateway traffic, significantly degrade the overall transmission capacity. To address these issues, this paper proposes a balanced satellite-ground scheduling architecture based on the analytic hierarchy process, called AHP-BSA. First, three spatiotemporal parameters (interconnection time$R(t)$, transmission capacity$C(t)$, and propagation delay$D_{p}$) are defined. The rationality and consistency of AHP-BSA is proved using the spatiotemporal parameters. In the parameter calculation phase of AHP-BSA, this paper further proves the relationship between$R(t)$and outage probability, as well as between$D_{p}$and transmission efficiency. Then, a parameter optimization algorithm is designed in AHP-BSA. These spatiotemporal parameters are normalized, weighted, and incorporated into the parameter optimization algorithm. Through stability-aware adjustment, it adjusts scheduling decisions in response to link dynamics and real-time fluctuations. Simulation results confirm that AHP-BSA outperforms existing methods in transmission capacity, time complexity, and long-term traffic balance.

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up-and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture; it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://github.com/lalalulao/MWT-Diff.

False correspondence removal is a persistent challenge in image feature-matching-based applications, especially in complex scenes. Traditional methods often rely on the consistency assumption to model the motion of correct correspondences, which neglects non-consistent correct correspondences, resulting in suboptimal performance. In this paper, we introduce the NCNet, a novel network designed to address this limitation by fitting the motion of both consistent and non-consistent correct correspondences using the learnable frequency response function. Unlike conventional approaches that focus on pixel-based 2D movements, NCNet utilizes the Motion Fitting Residual Module to estimate the high-dimensional motion, capturing the intricate 3D geometrical variations of matching pairs. To further enhance performance, we propose a loss function that balances the performance between the two types of correct correspondences. Extensive experiments demonstrate that NCNet significantly outperforms existing methods in terms of precision for false correspondence removal. The implementation of our approach is publicly available at: https://github.com/Livsdjo/NCNet-Code.

Low-light image enhancement (LLIE) has long been a challenging problem due to the complexities of noise and illumination variations in low-light environments. In this paper, we propose an innovative method that combines Retinex theory and diffusion models to enhance low-light images. Our method decomposes the input image into reflectance and illumination components, where the reflectance is denoised using an adaptive diffusion-based reflectance denoising module, and the illumination is enhanced via a newly designed illumination enhancement module. Notably, we introduce an adaptive noise estimation network to guide the denoising process of the diffusion model, effectively removing noise while preserving image details. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly outperforms existing techniques in terms of PSNR and SSIM, while reducing artifacts and preserving natural colors.

Accurate activation and optimization of autofocus (AF) functions are essential for capturing high-quality images and minimizing camera response time. Traditional contrast detection autofocus (CDAF) methods suffer from a trade-off between accuracy and robustness, while learning-based methods often incur high spatio-temporal computational costs. To address these issues, we propose a lightweight focus discriminative network (FDNet) tailored for AF tasks. Built upon the ShuffleNet V2 backbone, FDNet leverages a genetic algorithm optimization (GAO) strategy to automatically search for efficient network structures, and incorporates coordinate attention (CA) and multi-scale feature fusion (MFF) modules to enhance spatial, directional, and contextual feature extraction. A dedicated focus stack dataset is constructed with high-quality annotations to support training and evaluation. Experimental results show that FDNet outperforms mainstream methods by up to 4% in classification accuracy while requiring only 0.2 GFLOPs, 0.5 M parameters, a model size of 2.1 MB, and an inference time of 0.06 s, achieving a superior balance between performance and efficiency. Ablation studies further confirm the effectiveness of the GAO, CA, and MFF components in improving the accuracy and robustness of focus feature classification.

In recent years, low-light image enhancement techniques have made significant progress in generating reasonable visual details. However, current methods have not yet fully utilized the full semantic prior of visual elements in low-light environments. Therefore, images generated by these low-light image enhancement methods often suffer from degraded visual quality and may even be distorted. To address this problem, we propose a method to guide low-light image enhancement by predicting semantic priors. Specifically, we train a semantic prior predictor under standard lighting conditions, which is made to learn and predict semantic prior features for low-light images by knowledge distillation on high-quality standard images. Subsequently, we utilize a semantic-aware module that enables the model to adaptively integrate these learned semantic priors, thus ensuring semantic consistency of the enhanced images. Experiments show that the method outperforms several current state-of-the-art methods in terms of visual performance on the LOL-v2 and SICE benchmark datasets. Our code is available athttps://github.com/tianzhiya/SELL.

When restoring low-light images, most methods largely overlook the ambiguity due to dark noise and lack discrimination for region and shape representations, resulting in invalid feature enhancement. In this work, we propose a physically explainable and prior guidance model for low-light image enhancement, termed Dual-Conditional Guidance Sparse Diffusion (DCGSD). Specifically, we introduce an elaborately designed Luminance Structure Guidance Head, which can be easily plugged into the existing diffusion model to emphasize the value of the luminance and structural representation. Furthermore, for reliable noise analysis, we provide a novel Sparse Attention Enhancement Module that is adaptively empowered to exploit the most useful region-to-region dependencies. This dynamic selection makes the diffusion process from dense to sparse, thus improving the efficiency of the reasoning noise distributions. To avoid noise amplification, we further present a Skip Calibration Module, which can be used to refine the local neighborhood that contains noisy and structural information. Extensive experiments have been performed to verify the superiority of the proposed method. DCGSD shows that leveraging dual-conditional guidance can support the diffusion model to produce sharper and more realistic results.

Deep learning methods excel in Polarimetric SAR (PolSAR) image classification. However, existing methods typically sample an image block for each pixel with a fixed-size square window, which always contains inconsistent/incomplete content with the central pixel, resulting in many misclassifications especially in boundary and heterogeneous regions. So, a size-fixed square window is not enough for representing various terrain objects. To address this issue, we develop a content-adaptive multi-region deep network to obtain contextual consistent sampling windows for diverse terrain objects. Firstly, a complex scene of PolSAR image is partitioned into homogeneous, heterogeneous and boundary regions. Then, sampling windows with adaptive direction and scale are designed for three distinct regions. Besides, windows with central and global regions are proposed to provide additional local and global information. Finally, a fusion network is designed to adaptively combine different sampling windows to enhance classification performance. Experimental results on three real data sets demonstrate that the proposed method can achieve superior performance in both edge details and heterogeneous terrain objects compared with the state-of-the-art methods.

Diffusion model has achieved excellent performance in natural image processing, which can learn the noise distribution by the degradation and restoration processes. However, the model is limited to Gaussian noises. Actually, Polarimetric Synthetic Aperture Radar(PolSAR) images have complex non-Gaussian speckle noises, for which the Gaussian diffusion model is difficult to learn their intrinsic statistical characteristics. In this paper, we propose a novel scattering mechanism inspired non-Gaussian diffusion model for PolSAR image classification. To better simulate the PolSAR speckle noise, a mixed noise distribution is defined for PolSAR covariance matrices by combining Gamma multiplicative and Gaussian additive noises. A non-Gaussian forward noising process is derived to degrade a clean PolSAR image to a noisy image by steps. Then, the U-net structure is trained to remove noises for each step, effectively extracting non-Gaussian statistical features. However, statistical features can only characterize the overall distribution of the dataset, which is insufficient to describe complicated individual objects; the original PolSAR data reflect the detailed scattering mechanism for individual pixels, which can provide complementary object information for classification. Therefore, a scattering-statistical joint learning network is further developed with a dual-branch architecture to enhance discrimination ability. In particular, a multiscale pyramid module and attention mechanism are designed to improve the ability of feature learning. Experimental results on five real PolSAR datasets demonstrate that the proposed method effectively captures edge details and preserves homogeneous regions for terrain classification, especially in heterogeneous regions.

Infrared and visible image fusion is currently an important research direction in the field of multimodal image fusion, which aims to utilize the complementary information between infrared images and visible images to generate a new image containing richer information. In recent years, many deep learning-based methods for infrared and visible image fusion have emerged.However, most of these approaches ignore the importance of semantic information in image fusion, resulting in the generation of fused images that do not perform well enough in human visual perception and advanced visual tasks.To address this problem, we propose a semantic prior knowledge-driven infrared and visible image fusion method. The method utilizes a pre-trained semantic segmentation model to acquire semantic information of infrared and visible images, and drives the fusion process of infrared and visible images through semantic feature perception module and semantic feature embedding module.Meanwhile, we divide the fused image into each category block and consider them as components, and utilize the regional semantic adversarial loss to enhance the adversarial network generation ability in different regions, thus improving the quality of the fused image.Through extensive experiments on widely used datasets, the results show that our approach outperforms current leading algorithms in both human eye visualization and advanced visual tasks.

Accurately computing optical flow in low-contrast and noisy dark images is challenging, especially when contour information is degraded or difficult to extract. This paper proposes CEDFlow, a latent space contour enhancement for estimating optical flow in dark environments. By leveraging spatial frequency feature decomposition, CEDFlow effectively encodes local and global motion features. Importantly, we introduce the 2nd-order Gaussian difference operation to select salient contour features in the latent space precisely. It is specifically designed for large-scale contour components essential in dark optical flow estimation. Experimental results on the FCDN and VBOF datasets demonstrate that CEDFlow outperforms state-of-the-art methods in terms of the EPE index and produces more accurate and robust flow estimation. Our code is available at: https://github.com/xautstuzfy.

Medical images often occupy large storage space and contain patient privacy or sensitive information, which makes them difficult and unsafe to be transmitted through the network. This paper proposed an adaptive compression-encryption scheme based on improved compressive sensing (CS) and deoxyribonucleic acid (DNA) coding-compression, which helps to solve the above problem. In the proposed scheme, first the original medical image was compressed to a floating point CS matrix using the discrete wavelet transform and the partial Hadamard matrix. The CS matrix was then normalized and rounded for matrix quantification. The result of quantification was fed to DNA fixed encoding and DNA run length coding for encryption and secondary compression. Finally the compressed-encrypted image was obtained after DNA dynamic encoding-decoding and regroups operation. The proposed scheme was tested against 9 images and proved to be effective in reducing the quantization errors and enhancing the compression performance. For instance, when the benchmark compression ratio (CR) is 0.5, the CR can be reduced by 5%(CR = 0.4453) ∼ 23%(CR = 0.2636), with the corresponding peak signal-to-noise ratio values consistently surpassing the benchmark. Furthermore, the execution of DNA compression and DNA dynamic encoding-decoding provided double guarantee for the algorithm’s security.

Infrared and visible image fusion is an important multimodal image processing task that aims to enhance computer vision performance by effectively fusing infrared and visible images. Although in recent years, many deep learning-based methods for infrared and visible image fusion have emerged. Howeve, most of these methods ignore the important role of semantic information in image fusion. Therefore, this paper proposes a semantic priori guided infrared and visible image fusion network called SPGFusion. It uses an adversarial generative network framework based on semantic priors to guide the infrared and visible image fusion process by combining a semantic feature-aware module and semantic generative adversarial loss. Experimental results demonstrate that the SPG-Fusion method yields more visually appealing fusion results and outperform state-of-the-art image fusion algorithms in visual quality and quantitative evaluation. The source code is available at https://github.com/tianzhiya/SPGFusion.

Photographs taken in low-light conditions have a low signal-to-noise ratio and impaired visual quality. We observe that low-light images exhibit a lower signal-to-noise ratio, resulting in a mixture of fine details, textures, and noise, making it challenging to reconstruct small-scale textures in the image. Inspired by this observation, we propose a SNR-guided CNN-Transformer network for high frequency restoration during low light image enhancement. The proposed method first decomposes image into high-frequency and low frequency components by image decomposition module. The low-frequency image is processed by a trainable Low Frequency SNR Perception (LFSP) module, resulting in excellent denoising performance and generating SNR-enhanced images with clearer edge contours. Guided by the low-frequency SNR feature maps, the details and textures of the high-frequency components are enhanced using a combination of transformer networks and convolutional networks, thereby compensating the detail distortions in the high frequency components of the image. The subjective and objective experiments demonstrate that our proposed method outperforms existing approaches in terms of detail and structure preservation.

Dark Image Enhancement (DIE) aims to improve contrast and restore details for captured images under low illumination. Currently, traditional Transformer methods have achieved significant performance in the DIE problem; however, all-pairs correlation computation is redundant in learning key properties and restoring high-order representations. To alleviate this problem, we introduce a Channel-wise Guidance Sparse Transformer framework, namely CGSformer, which not only adaptively selects the key channel-wise representations through a threshold operator, but also keeps the most useful self-attention values for feature restoration guided by the selected information. Besides, we introduce a Bidirectional Gate Feed-Forward (BGFF) network to aggregate features to better facilitate high-quality image reconstruction. The experiments are conducted on representative datasets, showing that our CGSformer consistently achieves state-of-the-art performance on widely used benchmarks.

Remote sensing images encompass a multitude of targets with varying scales and lower resolutions, posing significant challenges for remote sensing image captioning tasks. To fully extract and leverage image features, this paper proposes a multi-scale feature extraction network that enhances the representational capacity of features by integrating different scales, enabling more accurate identification and description of targets. Additionally, we designed a Small Target Attention module to further enhance the network’s sensitivity to densely distributed and small-sized targets. Extensive experiments conducted on three publicly available datasets demonstrate that the proposed method outperforms the compared methods in capturing key information. Moreover, it shows better performance when processing remote sensing images with lower resolutions and small-sized targets.

Deep learning models have demonstrated excellent performance for polarimetric SAR image classification. However, existing approaches generally use a fixed square window to sample image blocks as the network input, which may not effectively extract various terrain objects. To alleviate this issue, we proposed an adaptive region sampling network to learn different terrain types by introducing a novel sampling scheme with varying direction and scale. Initially, a complex PolSAR image is segmented into homogeneous, heterogeneous and boundary regions. Subsequently, small-scale and large-scale sampling windows are designed for homogeneous and heterogeneous regions, to capture local and global features for two types of regions respectively. Additionally, an adaptive directional sampling window is designed for boundary regions to ensure context consistency in the image block and prevent edge confusion. Experiments conducted on real PolSAR data sets demonstrate that our method achieves superior classification results, providing both regional consistency and boundary preservation.

Deep learning networks can automatically acquire high-level semantic features for polarimetric SAR image classification, while it involves a blind learning procedure without explicit guidance. In contrast, sparse representation methods represent effective non-deep models with a robust mathematical mechanism serving as guidance. However, they can’t capture complex image features and semantic information. To address these issues, we propose a novel approach known as the CNN-enhanced Deep Sparse Representation Network (CE-DSRNet) for PolSAR image classification, which a Sparse Representation (SR) guided deep learning model. Initially, a sparse representation model is constructed for PolSAR images to capture essential features. Subsequently, to solve the sparse model, a Deep Sparse Representation Network (DSRNet) is devised by transforming the Soft Threshold Iterative (ISTA) optimization procedure into a network, enabling automatic learning of sparse coefficients as features. Finally, a CNN-enhanced DSRNet is introduced, integrating DSRNet with CNN to effectively extract deep semantic features and enhance classification accuracy. Experiments demonstrate the effectiveness of the proposed method compared to state-of-the-art approaches.

Deep learning models have been widely applied to Polarimetric Synthetic Aperture Radar (PolSAR) image classification due to their excellent performance. However, unlike natural images, PolSAR data is a 3×3 covariance matrix for each resolution unit. Existing deep learning methods generally convert the covariance matrix into a vector as the input of neural networks, which destroys the correlation between channels and distorts the matrix structure. To alleviate this issue, we explore a Symmetric Positive Definite (SPD) convolution network for PolSAR images, which directly inputs the PolSAR complex matrix into the network to learn the geometric features in Riemannian space. Furthermore, a CNN-enhanced SPDnet is designed to further learn the contextual high-level features, which can convert Riemannian matrix features into Euclidean space and apply them for classification. Experimental results on real PolSAR data sets demonstrate the proposed method can achieve better performance than the state-of-the-art methods.

The Convolutional Neural Network (CNN) model excels at learning local features, but struggles with capturing global large-scale features, particularly in extremely heterogeneous areas. In contrast, the Graph Convolution Network (GCN) is an effective tool for PolSAR image classification, demonstrating a capability to learn large-scale global features proficiently.To learn effective features for both heterogenous terrain objects and edge details well, a novel region partition based hybrid deep network is proposed for adaptive learning features for boundary and non-boundary regions, which can learn both large-scale global features for extremely heterogeneous terrain objects and pixel-wise features for edge details. The proposed method can effectively partition a PolSAR image into boundary and non-boundary regions, and design a CNN and GCN subnetworks for them respectively. Subsequently, a unified network is designed to effectively fuse both the advantages of GCN and CNN to enhance classification performance. The experiments verify the proposed algorithm can achieve better performance than compared methods in both region homogeneity and boundary preservation.

In the low light image enhancement, single exposure images contains a limited dynamic range, which hinders the restoration of contrast and texture. To address these problems, we propose a multi exposure generation and fusion method (MEGF) which simulates multi exposure images and perform feature fusion and enhancement on these images. First, we propose a Multi-Exposure Generation (MEG) method, which constructs the Gaussian Distribution for each exposure level based on multi exposure datasets. MEG can generate images with different exposure levels based on the constructed distribution. Then, the Perceptual Importance based Multi-Exposure Feature Enhancement (PIMEFE) block is developed to fuse the feature of generated multi exposure images using VGG-16 network. Before fusion, the generated images are input to Multi Scale Recursive Feature Enhancement (MSRFE) blocks and obtain the denoised and enhanced features. Finally, the fused feature are input to Curve Adjustment (CA) block for fine tuning and provide the color enhancement on fusion features. We propose the Multiple Exposure Recursive Fusion (MERF) block which estimates the adjusting factors for CA block. Experimental results demonstrate that our method outperforms other techniques in both subjective and objective evaluations on real and synthetic datasets.

Abstract With advancements in medical imaging technology, colour medical images make lesion diagnosis more intuitive. However, when transmitting these high‐capacity images, doctors and researchers must not only address the challenges of storage and transmission efficiency but also guard against unauthorized access and data security risks. To address these issues, a revolutionary approach for colour medical image compression encryption based on compressive sensing and deoxyribonucleic acid (DNA) coding operation is introduced in this study. Randomness and sparse optimizations are performed on three floating‐point matrices obtained through discrete wavelet and sparse transforms of plain colour medical images by employing position scrambling and reduced‐stiffness operations. Subsequently, the floating‐point matrices are measured and quantized to generate three 8‐bit integer matrices. Further, pixel‐by‐pixel DNA encoding, DNA‐base scrambling, DNA XOR, and DNA decoding operations are performed to achieve DNA base‐position scrambling and value diffusion. Finally, the regrouped bit planes help yield the compressed encrypted images. A comprehensive analysis of the proposed algorithm's encryption and decryption effectiveness, compression performance, and security was conducted. The results show that, with a compression ratio of 0.5, average PSNR = 42.7153 dB and average MSSIM = 0.9779, key space is , average entropy = 7.9986 bits, average histogram variance = 509.53, and the correlation coefficients are close to 0. Moreover, the algorithm shows some immunity to common cryptographic attacks, such as differential, known‐plaintext, noise, and occlusion attacks. Thus, the proposed algorithm addresses the challenges posed by the sensitive nature of patient information and limited storage space.

Recently, deep learning methods have achieved superior performance for polarimetric synthetic aperture radar (PolSAR) image classification. Existing deep learning methods learn PolSAR data by converting the covariance matrix into a feature vector or complex-valued vector as the input, learning features in Euclidean space. However, it is well-known that covariance matrices are manifold data endowing in Riemannian space instead of Euclidean space. Existing methods cannot learn the geometric characteristics of covariance matrices directly and destroy the channel correlation. To learn features from covariance matrices directly, we propose a lightweight Riemannian covariance matrix convolutional network (LRCM_CNN) for PolSAR classification for the first time, which directly utilizes the covariance matrix as the network input and defines the Riemannian operations to learn complex matrix’s features in Riemannian space. The proposed LRCM_CNN network initially designs a lightweight Riemannian covariance matrix network (LRCMnet) to learn covariance matrix features by exploiting a series of Riemannian convolution, rectified linear unit (ReLu), and LogEig operations in Riemannian space, which breaks through the Euclidean constraint of conventional networks. Then, features learned from covariance matrices are converted from Riemannian to Euclidean space, and a CNN module is appended to enhance contextual covariance matrix features. Besides, a fast kernel learning method is developed for the proposed method to learn class-specific features and reduce the computation time effectively, which implements the lightweight RCMnet. Experiments are conducted on four sets of real PolSAR data with different bands and sensors. Experiments results demonstrate the proposed method can obtain superior performance than the state-of-the-art methods.

Photographs taken in low-illumination environment have a low signal-to-noise ratio and impaired visual quality. Enhancing lowlight images tends to amplify noise. To address this problem, we propose a Multi-Scale Recursive Feature Enhancement (MSRFE) network for low light image enhancement. The MSRFE network consists of several Feature Enhancement (FE) blocks which are applied to enhance the multi-scale image feature and remove the noise recursively in each scale residual map between adjacent scale feature. Then, a deep recursive Curve Adjustment (CA) block is proposed further fine-tunes the output of MSRFE netowrk by learning a non-linear curve which can adjust the image luminance and details. We evaluate the proposed method on both real and synthetic datasets. The results show that our proposed method outperforms other state-of-the-art methods on both visual and objective evaluation indicators.

In low-light images, contrast and brightness are corrupted, making it difficult to accurately percept detail and edge information with the naked eye. Because of the development of multi sensor imaging, RGB-IR image fusion can enhance the imaging quality in low light conditions. However, the existing fusion algorithms have insufficient enhancement, distorted detail and low contrast, which make it difficult to generate high-quality fusion results. In this paper, we propose a Transformer-CNN image fusion method which considers global-local features fusion for low light image enhancement. The ConvGRU module is developed to alternatively select the global and local features with Transformer and CNN network. To effectively improve the network performance, a learnable weight adaptive loss function is proposed to adjust the weight of loss functions during training. Numerous experiments prove that our method can enrich fusion image information, improve image contrast and edge in low-light scenes compared to state of the art methods.

False correspondence removal remains a challenge for many feature-matching-based applications. This paper proposes a solution that revisits the local semantic context via position-attentive learning. First, a cross-divisional module is introduced to extract semantic features from image-patch pairs. Then, through a position-attentive mechanism, the parametric positions and extracted semantic features are jointly utilized to compute the probabilities of a set of putative correspondences. The proposed approach is evaluated on several indoor and outdoor challenging datasets, containing up to 70%+ false correspondences. In most cases, the solution outperforms existing algorithms in terms of matching precision and F1-score. Furthermore, an ablation study indicates that revisiting the semantic context improves precision by nearly 5%. The code is available at the link below1.

Superpixel-based graph convolution network (SGCN) can extract global features well and reduce computing time greatly, which has been widely used in image classification. However, SGCN ignores individual feature for each pixel within a superpixel. Pixel-wise convolutional neural network (CNN) can learn local features with fixed-square convolution kernel. Combine with both the advantages of SGCN and CNN, we proposed a novel SGCN-CNN method, which can combine the global and local features together. Superpixel-wise SGC-N and pixel-wise CNN cannot be combined into a network directly since they are with different scales. To alleviate this issue, encoder and decoder are designed by defining an association matrix, which can covert features between superpixel and pixel. In addition, complex matrix-based Wishart metric is used to construct the edge weights for SGCN. The proposed method can obtain both global and local features to improve classification performance. Experimental results demonstrate the effectiveness of the proposed method.

In the low-light conditions, images are corrupted by low contrast and severe noise, but event cameras can capture event streams with clear edge structures. Therefore, we propose an Event-Guided Attention Network for Low-light Image Enhancement (EGAN) using a dual branch Network and recover clear structure with the guide of events. To overcome the lack of paired training datasets, we first synthesize the dataset containing low-light event streams, low-light images, and the ground truth (GT) normal-light images. Then, we develop an end-to-end dual branch network consisting of a Image Enhancement Branch (IEB) and a Gradient Reconstruction Branch (GRB). The GRB branch reconstructs image gradients using events, and the IEB enhances low-light images using reconstructed gradients. Moreover, we develops the Attention based Event-Image Feature Fusion Module (AEIFFM) which selectively fuses the event and low-light image features using the spatial and channel attention mechanism, and the fused features are concatenated into the IEB and GRB, which respectively generate the enhanced images with clear structure and more accurate gradient images. Extensive experiments on synthetic and real datasets demonstrate that the proposed EGAN produces visually more appealing enhancement images, and achieves a good performance in structure preservation and denoising over state-of-the-arts.

Abstract In this work, a neural surface reconstruction framework is presented. In order to perform neural surface reconstruction using 2D supervision, a weighted random sampling based on saliency is introduced for training the deep neural network. In the proposed method, self‐attention is used to detect the saliency of input 2D images. The saliency map, that is, the weight matrix of the weighted random sampling, is used to sample the training samples. As a result, more samples in the reconstructed object area are collected. Moreover, an update strategy for weight based on sampling frequency is adopted to avoid the points that cannot be sampled all the time. The experiments are implemented in real‐world 2D images of objects with different material properties and lighting conditions based on the DTU dataset. The results show that the proposed method produces more detailed 3D surfaces, and the rendered results are closer to the raw images visually. In addition, the mean of peak signal‐to‐noise ratio (PNSR) is also improved.

Superpixel-based graph convolutional network (SGCN) has shown the advantages of less computational time and global modeling ability for polarimetric synthetic aperture radar (PolSAR) image classification. However, the effectiveness is heavily dependent on the superpixel segmentation result. Existing superpixel segmentation methods usually produce edge errors due to speckle and scattering confusion, which directly results in the mistakes of the final classification. To address this issue, a novel hybrid weighted fuzzy SGCN method(HF-SGCN) is proposed to correct the edge pixels by defining a fuzzy projection matrix (FPM). The FPM can transform features from superpixel to pixel, by which features of edge pixels can be calculated from all the neighboring superpixels with a certain probability, so as to correct edges to the most similar region. In addition, a hybrid weighted adjacent matrix is formulated by incorporating both the revised Wishart and multi-feature distances, which can enhance the discriminating features effectively. The proposed HF-SGCN method is capable of capturing the global contextual information and rectifying edges, while disregarding the local individual features for each pixel. To combine global and local features, we further propose the HF-SGCN-CNN method, which integrates the superpixel-wise HF-SGCN network and the pixel-wise 3D-CNN network into a unified framework. Thus, we can fuse the features extracted from two subnetworks, producing complementary global and local features that significantly improve classification accuracy. Experiments are conducted on four publicly real PolSAR datasets with different sensors and bands. Experimental results demonstrate the effectiveness of the proposed methods.

Nearest-regularized subspace (NRS) algorithm is an effective tool to obtain both accuracy and speed for PolSAR image classification. However, existing NRS-based methods only use the polarimetric feature vector as the input, which cannot learn the complex matrix structure and channel information. To learn the complex matrix and scattering features collaboratively, a novel complex matrix and polarimetric feature joint learning method is proposed for PolSAR image classification. Specifically, firstly, a Riemannian NRS model is utilized to learn matrix structure by constructing complex matrix dictionary and Riemannian distance metric. Then, the complex matrix and extracted scattering features are joint learned by the proposed model by constructing coupled dictionaries and different distance metrics. Besides, superpixels are utilized to suppress the speckle noises and reduce the computing time greatly. Experiments are conducted on the real PolSAR data and the results demonstrate the effectiveness of the proposed method.

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.

Abstract In order to generate a denser focal stack, a cooperative generative adversarial network is proposed to learn the refocusing ability from light field imaging. To keep the axial continuity, the proposed framework is designed to learn features of a focal stack in both axial directions. Different from the classic generative adversarial network, our generative module consists of a forward prediction sub‐network and a backward prediction sub‐network, taking the forward‐and‐backward focal stacks as their inputs, respectively. The bi‐directional predictions are then fused by a weighting process, which is guided by an adversarial module. The proposed network is trained on light field focal stacks conducted via digital refocusing. Without loss of the refocus continuity, one can axial super‐resolve a focal stack by using the trained model. The effectiveness of the proposed algorithm on different types of focal stacks produced by both light fields and traditional camera shootings is validated. The experimental results indicate that the refocus variation of a focal stack can be well learned and predicted without a complete light field. Therefore, the proposed algorithm outperforms the traditional digital refocusing in terms of run‐time.

Abstract Restoring damaged multichannel visual data with high loss ratio is quite a challenging task. To address this problem, an effective LRTC (low‐rank tensor completion) model integrated with total α‐order variation (TV α ) in the fractional bounded variation space BV α is proposed to perform superior fractional‐in‐space regularization. Based on using LR constraint to restore global patterns, TV α regularization is integrated to exploit nonlocally‐correlated information on each channel to infer the lost data and simultaneously effectively deal with complex details due to the powerful fractional calculus. Then, a nonlocal fractional regularization strategy for multi‐dimensional data and an effective numerical optimization method are creatively designed to solve this problem. Two novel fractional derivative matrix approximations are derived and applied to the first two unfolding modes of the tensor respectively to conveniently solve the fractional regularization subproblem by using an element‐wise shrinkage‐thresholding operation. In addition, boundary extension and adjustment strategy are designed for the unfolded matrices to alleviate the influence of inaccurate boundary conditions in fractional derivative computations. Experiments are conducted to illustrate its performance and efficiency for YUV video, RGB and HSI restoration, especially its ability to effectively recover complex structures and the details of multi‐component visual data with relatively high missing rate.

Nearest-regularized subspace (NRS) algorithm is a kind of effective representation learning method, which can obtain both accuracy and speed for PolSAR image classification. However, existing NRS methods use the polarimetric feature vector instead of the PolSAR original coherency matrix (known as Hermitian positive definite (HPD) matrix) as the input. This will destroy the matrix structure, and miss the instinct correlation among channels. How to utilize the original coherency matrix to NRS method is a key problem. To address this limitation, a Riemannian NRS method is proposed, which considers the HPD matrices endowed in a Riemannian space. First, to utilize the PolSAR original data, a Riemannian NRS method (RNRS) is proposed by constructing HPD dictionary and HPD distance metric. Then, a new Tikhonov regularization term is designed to reduce the differences within the same class. Finally, the optimization method is developed to resolve the proposed model, and the first-order derivative is inferred. Besides, only coherency matrix T is used as the input in the proposed method, while multiple features are utilized for compared methods in the experiments. Experimental results demonstrate the proposed method can outperform the state-of-the-art algorithms even with fewer features.

Many computer vision applications rely on feature detection and description, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors. In this paper, we propose a novel light field feature descriptor based on the Fourier disparity layer representation, for light field imaging applications. After the Harris feature detection in a scale-disparity space, the proposed feature descriptor is then extracted using a circular neighborhood rather than a square neighborhood. It is shown to yield more accurate feature matching, compared with the LiFF LF feature, with a lower computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated a synthetic stereo LF dataset with ground truth matching points. Experimental results with synthetic and real-world dataset show that our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.

Many computer vision applications rely on feature matching, hence the need for computationally efficient and robust 4D light field (LF) feature detectors and descriptors for applications using this imaging modality. In this paper, we propose a novel LF feature extraction method in the scale-disparity space, based on a Fourier disparity layer representation. The proposed feature extraction takes advantage of both the Harris feature detector and SIFT descriptor, and is shown to yield more accurate feature matching, compared with the LiFF light field feature with low computational complexity. In order to evaluate the feature matching performance with the proposed descriptor, we generated synthetic LF datasets with ground truth matching points. Experimental results with synthetic and real datasets show that, our solution outperforms existing methods in terms of both feature detection robustness and feature matching accuracy.

Scene flow estimation with RGB‐D frames is receiving increasing attention in digital video processing and computer vision due to the widespread use of depth sensors. Existing methods based on object segmentation have shown their effectiveness for object occlusion and large displacement. However, improper segmentation often causes incomplete segmented areas or incorrect edges, which will result in inaccurate occlusion inference and scene flow estimation. To this end, an automatic layered RGB‐D scene flow estimation method is proposed, which achieves more accurate layering of objects in depth image by exploring motion information. The authors exploit super‐pixel segmentation for initial layering, which is beneficial for preserving edges and integrity of the objects. Furthermore, an optical flow which is highly correlated with the scene is also used for automatic layering. Finally, the utilisation of super‐pixel segmentation and motion information would ensure the integrity of the object area and improve the accuracy of scene flow. They have validated their approach both qualitatively and quantitatively on several public datasets. Experimental results show that the proposed method is able to preserve the integrity of the object and achieve lower root mean square error and average angular error as compared with the current state‐of‐the‐art algorithms.