ABSTRACT Images captured with poor hardware and insufficient light sources suffer from visual degradation such as low visibility, strong noise, and color casts. Low‐light image enhancement methods focus on solving the problem of brightness in dark areas while eliminating the degradation of low‐light images. To solve the above problems, we proposed a hybrid CNN‐transformer multi information fusion network (HCTMIF) for low‐light image enhancement. In this paper, the proposed network architecture is divided into three stages to progressively improve the degraded features of low‐light images using the divide‐and‐conquer principle. First, both the first stage and the second stage adopt the encoder–decoder architecture composed of transformer and CNN to improve the long‐distance modeling and local feature extraction capabilities of the network. We add a visual enhancement module (VEM) to the encoding block to further strengthen the network's ability to learn global and local information. In addition, the multi‐information fusion block (MIFB) is used to complement the feature maps corresponding to the same scale of the coding block and decoding block of each layer. Second, to improve the mobility of useful information across stages, we designed the self‐supervised module (SSM) to readjust the weight parameters to enhance the characterization of local features. Finally, to retain the spatial details of the enhanced images more precisely, we design the detail supplement unit (DSU) to enrich the saturation of the enhanced images. After qualitative and quantitative analyses on multiple benchmark datasets, our method outperforms other methods in terms of visual effects and metric scores.

Recent advances in transformer architectures have significantly improved performance in time‐series forecasting. Despite the excellent performance of attention mechanisms in global modeling, they often overlook local correlations between seasonal cycles. Drawing on the idea of trend‐seasonality decomposition, we design a seasonal cycle‐aware time‐series forecasting model (SCA‐Net). This model uses a dual‐branch extraction architecture to decompose time series into seasonal and trend components, modeling them based on their intrinsic features, thereby improving prediction accuracy and model interpretability. We propose a method combining global modeling and local feature extraction within seasonal cycles to capture the global view and explore latent features. Specifically, we introduce a frequency‐domain attention mechanism for global modeling and use multiscale dilated convolution to capture local correlations within each cycle, ensuring more comprehensive and accurate feature extraction. For simpler trend components, we apply a regression method and merge the output with the seasonal components via residual connections. To improve seasonal cycle identification, we design an adaptive decomposition method that extracts trend components layer by layer, enabling better decomposition and more useful information extraction. Extensive experiments on eight classic datasets show that SCA‐Net achieves a performance improvement of 12.1% in multivariate forecasting and 15.6% in univariate forecasting compared to the baseline.

In this letter, we propose a convolutional dictionary iterative model for pansharpening with a mixture of experts. First, we define an observation model to model the common and unique feature information between multispectral (MS) and panchromatic (PAN) images. During this process, a proximal gradient algorithm is used to iteratively update the network parameters. The adaptive expert module (AEM) is designed to handle the unique and common features separately by using PAN mixture of experts (PMOE), multispectral mixture-of-experts (MMOE), and common mixture-of-experts (CMOE) modules, to achieve effective information reconstruction. Finally, the expert mixture fusion module (EMFM) adaptively integrates the information from the three mixture-of-experts (MOE) components by dynamically adjusting their respective weights, resulting in the final fused image. We conducted full-resolution and reduce-resolution experiments on GF2 and WV3 datasets with current state-of-the-art methods, and the experimental results show that our method performs best. The code is released onhttps://github.com/who15/CDME.

Remote sensing (RS) change detection (CD) faces challenges in effectively identifying non-salient change regions, such as subtle architectural modifications or changes that closely resemble the background. The primary difficulties stem from the weak feature representation of non-salient changes, which results in insufficient model response, and the high similarity between background and change regions, leading to misdetection or omission. To address this issue, we propose SPRMamba, a Mamba-based saliency proportion reconciliatory network with squeezed windows for remote sensing change detection. It introduces state space models with windowing operations and cross-window interaction mechanisms to improve the response to weak signals. To dynamically balance the representation of salient and non-salient features, we design the saliency proportion reconciler (SPR) to optimize the discrimination between background and change regions. In addition, we introduce a sparse saliency loss function, which imposes sparsity constraints on salient regions to enhance the feature representation of non-salient change regions. Experimental results show that SPRMamba significantly outperforms existing methods on several public datasets. Our code will be available at https://github.com/boomstarzzn/SPRMamba.

Currently, the task of remote sensing image segmentation still faces some challenges, such as variations in illumination, shadows, and occlusions present in remote sensing images. Additionally, there may be similarities and confusions between different types of terrain features. In this paper, we aim to explore how to utilize information exchange between multiple modalities to reduce the impact of interfering factors. To fully exploit the complementary information between different modalities, we establish an information exchange mechanism between optical images (visible light + infrared) features and Digital Surface Model (DSM) features. This allows them to interact and express themselves in a shared feature space, facilitating the acquisition of complementary information from different modalities. Furthermore, through a multimodal fusion encoder and decoder based on Transformer design, the optical features and DSM features are integrated, enabling the learning of high-level semantic representations in different dimensions. Extensive subjective, objective comparative experiments, and ablation experiments are conducted on the ISPRS Vaihingen and Potsdam datasets to evaluate the proposed method. The mIoU on the Vaihingen and Potsdam datasets reached 85.06% and 87.6% respectively, while the OA reached 92.01% and 91.92% respectively. The source code will be available at https://github.com/JunyuFan/MIEFNet.

Accurate remote sensing change detection (RSCD) tasks rely on comprehensively processing multiscale information from local details to effectively integrate global dependencies. Hybrid models based on convolutional neural networks (CNNs) and Transformers have become mainstream approaches in RSCD due to their complementary advantages in local feature extraction and long-term dependency modeling. However, the Transformer faces application bottlenecks due to the high secondary complexity of its attention mechanism. In recent years, state-space models (SSMs) with efficient hardware-aware design, represented by Mamba, have gained widespread attention for their excellent performance in long-series modeling and have demonstrated significant advantages in terms of improved accuracy, reduced memory consumption, and reduced computational cost. Based on the high match between the efficiency of SSM in long sequence data processing and the requirements of the RSCD task, this study explores the potential of its application in the RSCD task. However, relying on SSM alone is insufficient in recognizing fine-grained features in remote sensing images. To this end, we propose a novel hybrid architecture, ConMamba, which constructs a high-performance hybrid encoder (CS-Hybridizer) by realizing the deep integration of the CNN and SSM through the feature interaction module (FIM). In addition, we introduce the spatial integration module (SIM) in the feature reconstruction stage to further enhance the model’s ability to integrate complex contextual information. Extensive experimental results on three publicly available RSCD datasets show that ConMamba significantly outperforms existing techniques in several performance metrics, validating the effectiveness and foresight of the hybrid architecture based on the CNN and SSM in RSCD.

With the development of imaging systems and satellite technology, higher quality high-resolution RS images are being applied in building change detection (BCD) techniques. Methods based on convolutional neural network (CNN) have achieved excellent success in BCD techniques due to their excellent feature discrimination ability. However, CNN relies heavily on the geometry of prior conditions and is limited by the size of the convolution kernel, making it easy to ignore global information. This makes it difficult to capture the long-range dependence of different building targets and handle complex spatial relationships in high-resolution satellite RS images. Considering that graph convolutional neural networks (GCN) have powerful internal relationship learning capabilities, we propose a multi-scale attention fusion graph network (MAFGNet) in this paper. MAFGNet uses a dual graph convolution module (DGM), which includes a spatial graph convolution network (SGCN) and a channel graph convolution network (CGCN), to effectively explore the long-range relationship between the detection target and the global at the spatial and channel levels. We also design a multi-scale attention fusion encoder that includes channel and spatial attention fusion modules to effectively combine valuable information from multi-scale features. In addition, an atrous context self-attention pyramid (ACSP) is designed to combine multi-scale context to enhance the feature representation of change information. We conducted qualitative and quantitative comparative experiments on different datasets to validate the effectiveness of our model. The experimental results show that our method performs better than advanced methods in terms of overall accuracy and visualization details. Our code is available at https://github.com/ShangGY805/MAFG.

Remote sensing images are characterized by high dimensionality, complex textures, and large scales. Traditional Convolutional Neural Network (CNN) methods may overlook spatial relationships and contextual information among pixels when dealing with remote sensing data. Therefore, Graph Convolutional Networks (GCN) have emerged as a promising solution. In this paper, we propose a Contextual Spatial Awareness Remote Sensing Image Change Detection Network Based on Graph and Convolution interaction (CSAGC). We aim to enhance the handling of contextual information by introducing multiple augmentation modules. In CSAGC, we propose a high-performance encoder called Congraph that integrates a CNN and a Graph Neural Network (GNN). By preserving the respective features of both branches, we effectively fuse local detailed features and global positional features, achieving superior feature extraction capabilities. Additionally, we design two modules to facilitate the integration of multiscale spatial information: Contextual Spatial Awareness Module (CSAM) and Spatial Integration Module (SIM). CSAM, a crucial module connecting the encoder and decoder, jointly explores contextual features using the current feature branch and high-low level feature branches, leveraging spatial positional information for better content acquisition. SIM, located in the decoder module, aims to integrate the multiscale information outputted by CSAM, complementing the contextual information and improving the overall network’s ability to capture spatial contextual information. We conducted extensive experiments on three datasets, namely LEVIR-CD, WHU-CD, and GZ-CD. The experimental results demonstrate that CSAGC exhibits excellent performance, achieving significant performance improvements compared to state-of-the-art (SOTA) methods.

The proposed method aims to enhance the fusion of high-resolution multispectral (MS) images (HRMS) by extracting spatial and spectral features from panchromatic (PAN) images and MS images. However, existing pan-sharpening methods often suffer from the problem of missing spatial and spectral detail information. To better preserve these details, we introduce a dual-branch feature fusion pan-sharpening network based on deep unfolding. In this network, we utilize the algorithm unfolding iterative module (AUIF-Block) to continuously acquire detailed information from both MS and PAN images for image reconstruction. By leveraging the adaptive channel and spatial feature enhancement module (DEM-Block), the network can adjust spatial and channel features adaptively, leading to more accurate feature extraction and more complete image reconstruction. Finally, the detail-based fusion module (DBFM-Block) is employed to integrate and enrich the content of detailed information extracted from different channels, resulting in improved fusion performance. Experiments were conducted on QuickBird (QB) and WorldView-2 (WV2) datasets. Through qualitative analysis and quantitative comparisons, we demonstrate that this method outperforms existing approaches.

Abstract When the lighting conditions are poor and the environmental light is weak, the image captured by the imaging device often has lower brightness and is accompanied by a lot of noise. The paper designs a progressive self‐calibration network model (PSCNet) for recovering high‐quality low‐light‐enhanced images. First, shallow features in low‐light images can be better focused and extracted with the help of attention mechanism. Next, the feature mapping is passed to the encoder and decoder modules, where the transformer and encoder‐decoder jump connection structures can be better combined with the semantic information of the context to learn rich deep feature information. Finally, the self‐calibration module can adaptively cascade the features decoded by the decoder and input them into the residual attention module quickly and accurately. Meanwhile, the LBP features of the image are also fused into the feature information of the residual attention module to enhance the detailed texture information of the image. Qualitative analysis and quantitative comparison of a large number of experimental results show that this method outperforms existing methods.

Consensus reaching is essential in group decision-making (GDM) since it can mitigate conflicts between expert opinions and promotes the further implementation of decision-making results. Meanwhile, interaction between experts commonly occurs within social networks and in practical GDM problems. Therefore, it neecessary to consider the trust relationship between experts and utilize it to facilitate the consensus-reaching process (CRP). However, most existing social network-based GDM studies mainly use local measures (e.g., degree centrality) to determine the importance of experts, which cannot reflect their actual influence on a global topological structure. To address this issue, we propose a novel consensus-reaching strategy from the perspective of complex network analysis. First, the Extended Comparative Linguistic Expressions with Symbolic Translation (ELICIT) is adopted to flexibly facilitate the expression of experts’ uncertain evaluations. The hybrid centrality is then defined to determine the influence of experts in the social network by considering both node importance and edge weight. Since experts with greater influence have stronger information propagation capabilities, hybrid centrality is utilized to guide the CRP, which can better reflect information flows in the social network. Additionally, the BWM-CRITIC weighting method is developed to reflect the significance and relationship among criteria. Finally, we verify the effectiveness and superiority of the proposed method by means of a case study on a sustainable supplier selection problem.

The poor quality of images recorded in low-light environments affects their further applications. To improve the visibility of low-light images, we propose a recurrent network based on filter-cluster attention (FCA), the main body of which consists of three units: difference concern, gate recurrent, and iterative residual. The network performs multi-stage recursive learning on low-light images, and then extracts deeper feature information. To compute more accurate dependence, we design a novel FCA that focuses on the saliency of feature channels. FCA and self-attention are used to highlight the low-light regions and important channels of the feature. We also design a dense connection pyramid (DenCP) to extract the color features of the low-light inversion image, to compensate for the loss of the image’s color information. Experimental results on six public datasets show that our method has outstanding performance in subjective and quantitative comparisons.

In the current research on remote sensing image change detection, the effective learning of mutual interactions between bi-temporal features has often been overlooked. To address this concern, we introduce a Patch Exchange Block aimed at capturing the interplay between bi-temporal channels by exchanging feature patches. This approach preserves feature structural information and prevents the introduction of unnecessary noise. Specifically, the feature maps of bi-temporal images are unfolded into multiple patches, followed by mutual patch exchanges and subsequent fusion operations. Additionally, we seek to leverage Transformers to tackle the model’s lack of effective global feature extraction capability. However, the standard Transformer aggregates features based on all query-key pairs, making the model susceptible to irrelevant features’ interference. Considering this, we introduce a Sparse Transformer in the decoder. It guides the model’s attention to areas of interest by selectively weighting the values produced by the Q and K operations, thus reducing interference from irrelevant information and focusing on the most valuable insights. Through experiments conducted on multiple datasets, we substantiate the effectiveness of our proposed EFIN approach.

With the significant advancements of Deep Learning (DL) in the field of remote sensing imagery, a plethora of Change Detection (CD) methods based on CNNs, attention mechanisms, and transformers have emerged. Presently, a substantial amount of research has gradually relinquished control over parameter quantities in pursuit of enhanced outcomes, resulting in the inflation of networks with numerous stacked modules. This paper is dedicated to integrating lightweight approaches into the CD task.We introduce a Lightweight Hybrid Dual-Attention CNN and Transformer network (LHDACT) based on Depthwise Over-Parameterized Convolution (DO-Conv). In comparison to traditional convolution, DO-Conv combines both traditional and depthwise convolutions, achieving commendable performance enhancement with minimal additional cost. Furthermore, we leverage DO-Conv to enhance the Multi-Scale Average Pooling module (MSAP), ensuring global context with low computational overhead.To better discern regions of interest within complex images, we enhance the Dual Attention Module (DAM) by sharing weights across spatial and channel dimensions, thereby bolstering feature region identification. Lastly, we employ a compact transformer module to capture feature differences, enabling precise change detection CD. Our approach is evaluated on the LEVIR-CD, WHU-CD, and GZ-CD datasets, yielding F1 scores of 91.23%, 87.51%, and 85.32%, respectively. These results demonstrate high performance on a cost-effective scale.

Pansharpening methods play a crucial role for remote sensing image processing. The existing pansharpening methods, in general, have the problems of spectral distortion and lack of spatial detail information. To mitigate these problems, we propose a multiscale hybrid attention Transformer pansharpening network (MHATP-Net). In the proposed network, the shallow feature (SF) is first acquired through an SF extraction module (SFEM), which contains the convolutional block attention module (CBAM) and dynamic convolution blocks. The CBAM in this module can filter initial information roughly, and the dynamic convolution blocks can enrich the SF information. Then, the multiscale Transformer module is used to obtain multiencoding feature images. We propose a hybrid attention module (HAM) in the multiscale feature recovery module to effectively address the balance between the spectral feature retention and the spatial feature recovery. In the training process, we use deep semantic statistics matching (D2SM) loss to optimize the output model. We have conducted extensive experiments on several known datasets, and the results show that this article has good performance compared with other state of the art (SOTA) methods.

Abstract Owing to the influence of light absorption and scattering in underwater environments, underwater images exhibit color deviation, low contrast and detail blur, and other degradations. This paper proposes an underwater image enhancement method combining a residual convolution network, local binary pattern (LBP), and self‐attention mechanism. The LBP operator processes the input underwater images. The LBP feature images and underwater images thus obtained constitute the network input. The network consists of three modules: a color correction module to remove the color deviation in underwater images, detail repair module to restore the integrity of details, and an LBP auxiliary enhancement module for global enhancement of image details. The correction and repair modules generate the correct color image and detailed supplement images, respectively. The final‐result image is obtained by superpositioning the two generated images. The experimental results confirm that our method can reproduce the bright colors and complete details of the visual effect, showing a significant improvement over other advanced methods in quantitative evaluation.

Abstract Hazy images often have color distortion, blur and other visible visual quality degradation, affecting the performance of some advanced visual tasks. Therefore, single image dehazing has always been a challenging and significant problem. Convolutional neural network has been widely used in image dehazing task, but the limitations of convolutional operation limit the development of dehazing task. Nowadays, Transformer offers a holistic approach to CV development and does not grow in location as the network deepens. For this reason, a hierarchical Transformer is introduced for use in the dehazing network. Specifically, the codec is improved and Transformer and CNN are combined to achieve basic feature extraction in the first stage. The encoder only models the global relationship at each layer, reducing the resolution of the feature map continuously and expanding the field of perception. In addition, an inter‐block supervision mechanism is added between encoder unit and decoder unit to refine features and supervise and select them, thus improving the efficiency of feature transmission. In the second stage, the original resolution block is used to extract the local features, and then feature fusion and interaction are carried out. In addition, to ensure the authenticity of the transmission of characteristic signals in the first stage and improve the transmission efficiency of the network, fusion attention mechanism is added between stages. It adds the residual image of the early input features to the image acquired in the first stage, then passes to the next stage. Ablation experiments show that the two‐stage network has significant benefits for image quality and visual effects. The experimental results on RESIDE, O‐Haze, and I‐Haze datasets show that the method is superior to advanced methods in dehazing effectiveness.

Abstract Boundary artifacts and color detail distortion are easily caused by the common multi‐focus image fusion methods. In order to solve this problem, we propose a two‐stage progressive residual learning network for multi‐focus image fusion. The proposed network can progressively learn color information and detail features through end‐to‐end mapping. The whole network is composed of two sub‐networks: the initial fusion block network and the enhanced fusion block network. First, the color information in the source image is fused by the initial fusion block network to generate the initial fusion image. Then on the basis of the initial fusion image, the detailed features of the source image are further fused by enhanced the fusion network to form the final fusion image. In order to solve the problem of lack of groundtruth when multi‐focus image fusion is carried out with supervised method, the multi‐focus image fusion problem is compared to the easy‐to‐solve image restoration problem. A synthetic dataset for network training is generated by ”degenerating” the VOC2012 dataset according to the set rules. After training, the method works well for fusion tasks without further processing. Experimental results show that the proposed method is superior to the existing methods in subjective visual perception and objective quantitative evaluation.

Abstract In realistic low‐light environments, images captured by imaging devices often have problems such as low brightness and low contrast, serious loss of detail information, and a large amount of noise, posing major challenges to computer vision tasks. Low‐light image enhancement can effectively improve the overall quality of the image, which has important significance and application value. In this study, an attention‐based multi‐channel feature fusion enhancement network (M‐FFENet) is proposed to process low‐light images. In this network, a feature extraction model is first used to obtain the deep features of the downsampled low‐light images and fit them to an affine bilateral grid. Second, the addition of attention‐based residual dense blocks (ARDB) allows the network to focus on more details and spatial information. Meanwhile, all color channels are considered. The channel features and bilateral meshes are then linearly interpolated using the feature reconfiguration model (FRM) to obtain high‐quality features containing rich color and texture information. Next, the feature fusion module (FFM) is used to fuse features that contain different information. Enhancement model is used to further recover texture and detail in the image. Finally, the enhanced image is output. Numerous experimental results have shown that the method achieves better results in both quantitative and qualitative aspects compared to other methods.

Abstract At night or in other low‐illumination environments, optical imaging devices cannot capture details and color information in images accurately because of the reduced number of photons captured and the low signal‐to‐noise ratio. Consequently, the image is very noisy with low contrast and inaccurate color information, which affects human visual perception and creates significant challenges in computer vision tasks. Low‐light image enhancement has great research value because it aims to reduce image noise and improve image quality. In this study, we propose an LBP‐based progressive feature aggregation network (P‐FANet) for low‐light image enhancement. The LBP feature has insensitivity to illumination, and it contains rich texture information. In the network, we input the LBP feature into each iteration of the network in an accompanying manner, which helps to restore some detailed information of the low‐light image. First, we input the low‐light image into the dual attention mechanism model to extract global features. Second, the extracted different features enter the feature aggregation module (FAM) for feature fusion. Third, we use the recurrent layer to share the features extracted at different stages, and use the residual layer to further extract deeper features. Finally, the enhanced image is output. The rationality of the method in this study has been verified through ablation experiments. Many experimental results show that the method in this study has greater advantages in subjective and objective evaluations compared with many other advanced methods.

Change detection (CD) of high-resolution remote sensing (RS) images is a basic task in RS image processing tasks. In recent years, CD tasks have made many attempts in pure convolutional networks, attention mechanism, and transformer, and have achieved good results. Based on the power of attention and transformers, we hope to find a method that can handle the details of the image better and has better generalization ability. In this article, we propose a dual-feature mixed attention-based transformer network (DMATNet). First, we adopt a dual-feature extraction method, using a simple convolutional neural network (CNN) to extract coarse features, and a CNN based on progressive sampling to extract fine features. Then, we fuse the fine and coarse features with dual-feature mixed attention (DFMA) module. It can not only extract more specific regions of interest, but also overcome the misjudgment caused by oversampling, and synchronize feature extraction and target information integration. Finally, we use transformer to optimize these extracted information and feedback into the original features in the encoder to help remodel the pixel space. We merged the DMAT network into a deep feature difference-based CD framework and conducted extensive experiments on four datasets, LEVIR-CD, DSIFN-CD, WHU-CD, and CLCD, respectively, with tested F1 and interconnection over union (IoU) results of 90.75%/84.13%, 71.23%/55.32%, 85.70%/74.98%, and 66.56%/59.87%. Experimental results show that our DMAT-based model performs significantly better than the existing state-of-the-art attention and transformer-based methods.

The pansharpening entails obtaining images with uniform spectral distribution and rich spatial details by fusing multispectral images and panchromatic images, which has become a major image fusion problem in the field of remote sensing. Convolutional neural networks are widely used in image processing. We propose a transformer-based regression network (DR-NET) architecture. The first stage was feature extraction, which entailed extracting spectral information and spatial details from multispectral images and panchromatic images. The second stage was feature fusion, which entailed integrating the extracted feature information. In the third stage, image reconstruction, images with uniform distribution of spectral information, and sufficient spatial details were obtained. The fourth stage entailed optimizing the network performance and calculating the loss of shallow feature image and the image result after downsampling during image reconstruction. The performance of the DR-NET was optimized by optimizing the sum of all the loss values, which could be considered double regression. Simulated and real data experiments were conducted on the GF-2, QuickBird, and WorldView2 datasets to compare the proposed method with classical pansharpening methods. The qualitative and quantitative analyses proved that the spectral distribution of the image pansharpened using our method was uniform, the spatial details were completely retained, and the evaluation indicators were also optimal, which fully demonstrated the superior performance of the DR-NET.

The pansharpening method combines complementary features from panchromatic (PAN) images and multispectral (MS) images to provide high-resolution MS images. Therefore, how to extract the features completely and reconstruct the image with high quality is the key link to obtain the ideal fusion image. We propose an attention-based and staged iterative network (ASIN) framework, which considers each subnetwork of the iterative network as a multistage process of pansharpening and carries out feature extraction and image reconstruction in each stage. Using the advantages of an iterative network for cross-stage depth features for the hierarchical extraction of refined features of MS images and PAN images for image reconstruction, we use the large kernel attention (LKA) module and the cascaded asymmetric coupling representation module to build the framework for feature extraction and the attention fusion module (AFM) to fuse the features of PAN and MS in the image reconstruction stage. LKA has channel and spatial adaptability, as well as strong long-range dependency establishment capability, which makes the feature extraction more complete. Asymmetric coupled representation module (ACRM) outputs refined spectral and spatial features by learning the hybrid correlation of MS and PAN images. AFM effectively utilizes the spectral and spatial features of the input, enabling the network to reduce information loss and retain important information. On the QuickBird (QB), WorldView-2 (WV2), and Gaofen-2 (GF-2) datasets, the superior performance of our method over the contrasting methods is demonstrated by quantitative comparison and qualitative analysis.

Pansharpening is a significant branch in the field of remote sensing image processing, the goal of which is to fuse panchromatic (PAN) and multispectral (MS) images through certain rules to generate high-resolution MS (HRMS) images. Therefore, how to improve the spatial and spectral resolutions of the fused image is the problem that we need to solve urgently. In this article, a multistage remote sensing image fusion network (MRFNet) is proposed on the basis of in-depth research and exploration on the fusion of the PAN and MS images to obtain a clear fused image that can reflect the ground features more comprehensively and completely. The proposed network consists of three stages that are connected by cross-stage fusion. The first two stages are used to extract the features of the PAN and MS images. The structure of the encoder–decoder and the channel attention module are used to extract the features of the remote sensing image in the channel domain. The third stage is the image reconstruction stage fusing the extracted features with the original image to improve the spatial and spectral resolutions of the fused result. A series of experiments are conducted on the benchmark datasets WorldView II, GF-2, and QuickBird. Qualitative analysis and quantitative comparison show the superiority of MRFNet in visual effects and the values of evaluation indicators.

Abstract Aiming at the problems of residual noise, low contrast, and limited detail information caused by low‐light images, this paper proposes a new Retinex variational model. According to Retinex theory, it is necessary to estimate the illumination and reflectance components decomposed from the original image. In order to better maintain the edge information, texture richness, and prevent artefacts, the exponential forms of local variation deviation and total variation are used as illumination prior and reflectance prior, respectively, and mixed norms are used to constrain them, so as to deal with the illumination information and texture details of the image more effectively, and then use the bright channel prior to improve the colour reproduction sense of the original image, thereby constructing the objective function, and finally using the alternating iterative optimization method to find the optimal solution to the proposed model. Experiments show that compared with other existing image enhancement methods, the method proposed here improves the contrast of the image, overcomes the phenomenon of halo artefacts and colour distortion, is more consistent with human vision, and produces better results in terms of quantitative performance.

Abstract In dynamic scene deblurring, recent neural network–based methods have been very successful. But with the improvement of deep deblurring performance, network structure and learning become more complicated. Compared with large‐scale network parameters and complex network structures, an iterative multi‐scale residual network to achieve a more effective parameter sharing scheme is proposed. In each iterative unit, fast multi‐scale residual blocks to replace superimposed convolutional layers or classic residual blocks are used. On the basis of preventing model overfitting, the receptive field of the network is increased. At the same time, the gated recurrent unit is introduced to connect modules of different stages. The model does not rely on the estimation of the blur kernel and directly generates sharp images in an end‐to‐end manner. The experimental structure on the benchmark dataset and real‐world images showed that this method has better quality than the existing methods in terms of large‐scale blur and subjective perception effects, both in quantitative and qualitative terms.

Low-light images have low brightness and contrast, which presents a huge obstacle to computer vision tasks. Low-light image enhancement is challenging because multiple factors (such as brightness, contrast, artifacts, and noise) must be considered simultaneously. In this study, we propose a neural network—a progressive-recursive image enhancement network (PRIEN)—to enhance low-light images. The main idea is to use a recursive unit, composed of a recursive layer and a residual block, to repeatedly unfold the input image for feature extraction. Unlike in previous methods, in the proposed study, we directly input low-light images into the dual attention model for global feature extraction. Next, we use a combination of recurrent layers and residual blocks for local feature extraction. Finally, we output the enhanced image. Furthermore, we input the global feature map of dual attention into each stage in a progressive way. In the local feature extraction module, a recurrent layer shares depth features across stages. In addition, we perform recursive operations on a single residual block, significantly reducing the number of parameters while ensuring good network performance. Although the network structure is simple, it can produce good results for a range of low-light conditions. We conducted experiments on widely adopted datasets. The results demonstrate the advantages of our method compared with other methods, from both qualitative and quantitative perspectives.