• We introduce PGSFNet for real-world scene text image super-resolution. • Adaptive Frequency Modulator is proposed to extract informative frequency components. • Text Information Enhancement module is designed to incorporate text priors. • We develop a Sobel loss to guide optimization towards sharper text details. • PGSFNet is shown to achieve superior performance on public text image datasets. Real-world scene text image super-resolution is challenging due to complex writing strokes, random text distribution, and diverse scene degradations. Existing text super-resolution methods focus on pure text images or fixed-size single-line text, which limits their practical utility. To address that, we propose a Prompt-Guided Selective Frequency super-resolution Network (PGSFNet). Our unique bicephalous neural model comprises a super-resolution branch and a prompt guidance branch. The latter specifically helps in leveraging text content-aware information priors. To that end, we propose a Text Information Enhancement module. To exploit selective frequency information present in the image, PGSFNet employs a proposed Adaptive Frequency Modulator fused with multi-attention structures. Considering the criticality of text edges in our task, we also propose a tailored text edge perception loss. Extensive experiments on the standard open real-world scene text image datasets demonstrate remarkable performance of our method, achieving up to 8.75% PNSR gain for × 2 and 2.28% SSIM gain for × 4 super-resolution on the Real-CE dataset. Our code will be made public at https://github.com/holastq/PGSFNet .

The inherent speckle noise and grayscale characteristics of synthetic aperture radar (SAR) images pose challenges to information perception and interpretation. To address this issue, we propose a novel conditional diffusion model with spatial-frequency information interaction for SAR colorization, namely SFI4SC. Specifically, we introduce a learnable wavelet transform module to obtain the global frequency information of the input SAR image and then take it as a guiding condition to assist the diffusion model for SAR colorization. Additionally, we improved the network structure for the diffusion model by designing and introducing a spatial and frequency information interaction module to achieve multidimensional information interaction for the network inputs, further enhancing perceptual details and geometric structures of the SAR colorization results. Experimental results in the real-world remote sensing dataset show that our approach can successfully enhance visual clarity and informational content while preserving the unique characteristics of the SAR images. The code will be made publicly available.

Due to obstructions such as trees and buildings, single-modal satellite or aerial images are insufficient for continuous high-precision representation of road features. To address this problem, this article proposes a lightweight cross-modal information interaction for road feature extraction (LCIRE-Net) from high-resolution remote sensing images (HRSIs) and GPS trajectory/LiDAR images. We design two parallel encoders for modality feature learning, using pairs of multimodal information as inputs to the encoders. By designing a cross-modal information dynamic interaction (CMIDI) mechanism, thresholds are used to decide whether to supplement redundant information from another modality, solving the issue of ineffective fusion calculations due to minor differences in multimodal feedback. A multimodal feature fusion module (MFFM) is proposed after the encoder output to achieve effective dual-modal fusion while addressing the interference of redundant noise generated during extraction. Subsequently, we present the feature refinement and enhancement module (FREM), which successfully captures edge features of the image using the receptive field of dilated convolution kernels. Additionally, in terms of lightweight design, we employ a novel SOTA method on D-LinkNet by replacing the original residual blocks with an enhanced ghost basic block. Extensive experiments are conducted on the BJRoad, Porto, and TLCGIS datasets, demonstrating that our network, with smaller parameters and FLOPs, outperforms other road-based semantic segmentation methods.

In a real-world infrared (IR) imaging system, effectively learning a consistent stripe noise removal model is essential. Most existing destriping methods cannot precisely reconstruct images due to cross-level semantic gaps and insufficient characterization of the global column features. To tackle this problem, we propose a novel IR image destriping method, called asymmetric sampling correction network (ASCNet), that can effectively capture global column relationships and embed them into a U-shaped framework, providing comprehensive discriminative representation and seamless semantic connectivity. Our ASCNet consists of three core elements: residual Haar discrete wavelet transform (RHDWT), pixel shuffle (PS), and column nonuniformity correction module (CNCM). Specifically, RHDWT is a novel downsampler that employs double-branch modeling to effectively integrate stripe-directional prior knowledge and data-driven semantic interaction to enrich the feature representation. Observing the semantic patterns crosstalk of stripe noise, PS is introduced as an upsampler to prevent excessive a priori decoding and performing semantic-bias-free image reconstruction. After each sampling, CNCM captures the column relationships in long-range dependencies. By incorporating column, spatial, and self-dependence information, CNCM well establishes a global context to distinguish stripes from the scene’s vertical structures. Extensive experiments on synthetic data, real data, and IR small target detection (IRSTD) tasks demonstrate that the proposed method outperforms state-of-the-art single-image destriping methods both visually and quantitatively. The code is available athttps://github.com/xdFai/ASCNet.

Deep learning (DL) networks have recently achieved excellent performance on image compressed sensing. However, most existing methods rely on burdened and complex network structures, resulting in significant computational and storage requirements that defeat the purpose of compressed sensing. This severely hinders their applicability in real-world resource-limited devices. In this paper, a lightweight cycle network driven by physical and deep priors for image compressed sensing is proposed which integrates the learning of the sensing matrix and compressive image reconstruction. Specifically, the regularization terms and a likelihood term derived from the physical observation model are learned in an end-to-end cycle network, simultaneously estimating the reconstructed image and sensing matrix in the image and feature domains. Moreover, a dual-domain fusion reconstruction module is proposed. It creates simulated measurement residuals for enhancing reconstruction in the compressed domain, which leads to high reconstruction performance and reduces computational load by bonding together the compressed image domains in the cyclic network. Extensive experiments demonstrate that our model delivers superior performance and alleviates model complexity, which is of great importance in low-budget applications.

Hyperspectral images (HSIs) often suffer from degradation caused by mixed noise, leading to a decline in the performance of subsequent advanced applications. To eliminate noise and improve image quality, transformer-based approaches have been successfully employed. Nevertheless, these strategies often involve large-scale modeling and tedious layer normalization, which causes inefficiencies during the denoising process. Additionally, the neglect of local spectral correlations in HSIs damages the physical properties in recovery, resulting in poor generalization and inefficient denoising performance. To address these problems, we propose an efficient spatial–spectral oriented triple attention network, dubbed S2OTAN, for HSI denoising. Specifically, to fully exploit the physical properties of HSIs, we impose spatial and spectral multiscale hybrid attention in the single-transformer block side-by-side to fuse spatial–spectral information in a parallel manner. For spatial feature extraction, we introduce hybrid spatial attention by constructing attention maps for pixels within and across windows to exploit the local and global similarity in spatial and improve computational efficiency. For spectral feature exploration, we utilize spectral partitioning operations to enhance the adjacent spectral dependences of HSIs and capture contextual information related to correlations. Consequently, our method exhibits a robust feature representation capability for removing mixed noise in HSIs. Extensive experiments on synthetic and real-world noisy scenarios demonstrate that the proposed approach outperforms other state-of-the-art approaches among quantitative metrics and visual effects. For the sake of reproducibility, the code is available at:https://github.com/Zilong-Xiao/S2OTAN.

Improving hyperspectral image (HSI) quality is crucial in subsequent applications. Current transformer-based methods effectively remove mixed noise from the original HSIs. However, there is limited research on targeted modeling of the spatial locality and edge properties of HSI. In addition, the original transformer computes global query-key pairs indifferently, resulting in equal weights for dissimilarity features, noise, and essential information, thus interfering with the restoration of clean images. To address these issues, this study proposes an effective HSI denoising network called multiscale-sparse spatial-spectral transformer (MS3T) to achieve end-to-end mixed noise removal. Specifically, we reconstruct the attention module in the original transformer by adopting a dual-stream approach to selectively explore the 3-D information of HSI from both spatial and spectral perspectives. In the spatial domain, we construct a multiscale context-capturing module based on the local and non-local similarity properties of HSI to establish remote connections from local to global. In the spectral domain, we develop a top-k selection operator to calculate the similarity scores of query-key pairs and select important semantic information for efficient feature aggregation. Both of the above modules alleviate the computational complexity issue of the original transformer to different extents, and improve the mixed noise removal performance of the whole network. To validate the effectiveness and efficiency of MS3T, we conduct synthetic and real experiments on multiple datasets, and the final results demonstrate that our method outperforms the state-of-the-art methods in both metric evaluation and visual effects; the reproducible code is available athttps://github.com/Zilong-Xiao/MS3T.

Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. However, largely overlooking effective global information modeling, existing techniques struggle when the target has high similarities with the background. We present aSpatial-channelCrossTransformerNetwork (SCTransNet) that leverages spatial-channel cross transformer blocks (SCTBs) on top of long-range skip connections to address the aforementioned challenge. In the proposed SCTBs, the outputs of all encoders are interacted with cross transformer to generate mixed features, which are redistributed to all decoders to effectively reinforce semantic differences between the target and clutter at full levels. Specifically, SCTB contains the following two key elements: (a) spatial-embedded single-head channel-cross attention (SSCA) for exchanging local spatial features and full-level global channel information to eliminate ambiguity among the encoders and facilitate high-level semantic associations of the images, and (b) a complementary feed-forward network (CFN) for enhancing the feature discriminability via a multi-scale strategy and cross-spatial-channel information interaction to promote beneficial information transfer. Our SCTransNet effectively encodes the semantic differences between targets and backgrounds to boost its internal representation for detecting small infrared targets accurately. Extensive experiments on three public datasets, NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, demonstrate that the proposed SCTransNet outperforms existing IRSTD methods. Our code will be made public at https://github.com/xdFai/SCTransNet.

Deep learning-based single-image infrared (IR) destriping has made significant advances. However, these methods are typically evaluated using “synthetic” images with specific stripe noise, making it unclear how well they handle “real” IR images. In fact, a clear and fair benchmarking of the existing destriping methods on real images, especially for the downstream IR small target detection (IRSTD) task, is currently an open gap. To tackle this problem, we introduce a novel benchmark, called IRSTD-oriented image destriping (IRSTDID-800), which thoroughly showcases the real distribution of IR small targets under stripe noise perturbation for the first time. Concretely, it consists of two subsets. (1) IRSTDID-SKY: composed of 500 real-world images afflicted with stripe noise, including unmanned aerial vehicles (UAVs) of various shapes, sizes, and contrasts. Moreover, these images are annotated with precise pixel levels for objective evaluation of IRSTD. (2) IRSTDID-GND: comprising 300 real-world images featuring common daily life objects, providing a richer scene under stripe noise. Based on the proposed IRSTDID-800, we comprehensively assess the performance of ten state-of-the-art (SOTA) destriping methods across eleven metrics, including full-reference, no-reference, and task-driven metrics with six advanced IRSTD methods. Furthermore, inspired by the correlation between image quality assessment and IRSTD, we proposed a task-oriented destriping optimization strategy. A loss function is introduced for IR image destriping, leveraging the structural properties of noise as a penalty term to strengthen image destriping and IRSTD. Overall, our analysis reveals interesting observations to guide future research in destriping and IRSTD tasks. Our dataset is available athttps://github.com/xdFai/IRSTDID-800.

Due to the influence of waves, the fluctuations of each part of the ship are heterogeneous, which makes the motion error is two-dimensional space-variant and the phase error is hard to be compensated uniformly. This phenomenon causes the moving ship target SAR imaging always contains blurs. In this paper, an autofocus method for non-linearly moving ship targets is proposed. By the backprojection (BP) imaging in the hybrid coordinate (HC) grid, multiple scattering regions are selected and local phase error estimation is performed. Then the phase errors of all pixels in the scene are obtained by polynomial regression. Finally, the image will be compensated precisely. Simulation experiments verify the effectiveness of the proposed method.

Thermal infrared (TIR) imaging has been an indispensable tool in surveillance and remote sensing fields due to the characteristic of this spectrum that enables the sensing system to detect relatively warm targets, especially in low-light conditions. However, the acquired TIR images often suffer from observable stripe noise, which reduces the target detectability to some extent. To remove the noise and keep the image details, this letter proposes a novel method that combines the spectral processing technology with the image-guidance mechanism. Specifically, the frequency band contaminated by stripe noise is corrected with the corresponding Fourier coefficients of a guided image, which can be estimated by existing smoothing methods. Various experiments on the simulated and real TIR images show high performance and efficiency of the proposed method. In addition, in the application of small target detection, it is demonstrated that local contrast between the target and its background is well maintained and the signal-to-clutter ratio is increased when our method is performed.

For the problem that the limited star map field angle cannot obtain the complete star map accurately, the paper study astral intrinsic and imaging features, a star map stitching algorithm based on the principle of visual perception is proposed firstly. The matching models of time and space dimensions is constructed by simulating the visual perception, then the stars and the planets points are saved by searching the matching star group dynamically, the star map is stitched and reconstructed efficiently by creating the computer sparse storage model. The experimental results show that the algorithm can achieve data compression quickly, compression ratio is 99.54%, which can reduce complexity of manual processing and can achieve star map stitching accurately.