Most existing event-driven image deblurring methods ignore inherent differences between the two modalities and lack explicit alignment strategies, leading to cross-modal mismatches and degraded feature reconstruction. To address this issue, a multi-stage dual encoder–decoder image deblurring method based on event image cross-modal fusion is proposed. The proposed network consists of an encoder and a decoder. The encoder employs dilated convolutional residual modules for feature extraction. It also integrates a cross-modal feature fusion module and a local scoring mechanism. These components combine event features with frame image features while suppressing noise. The decoder reconstructs image features via two directional decoding sub-networks. It also incorporates a feedback attention module. This module selects informative features along the feedback path. As a result, the image reconstruction quality is enhanced. In addition to the standard loss, mean absolute error, structural similarity, and frequency reconstruction losses are used to optimize deblurring performance. Extensive experiments are conducted on the GoPro, REBlur, and RwEvent datasets. For PSNR, our method exceeds REFID by 0.23 dB, 0.20 dB, and 0.49 dB on the three datasets. For SSIM, our model achieves gains of 0.002, 0.002, and 0.017 against REFID. In terms of computational cost and inference speed, our network adds only 3.4 M parameters and 105.2 GFLOPs, with an FPS reduction of only 2.12. This trivial efficiency loss delivers significant improvements in both pixel and structural restoration performance. Ablation experiments verify the independent positive contribution of each designed module. Both qualitative visual comparisons and quantitative metrics demonstrate that the proposed network has stronger deblurring and generalization capabilities.

Three-dimensional medical image segmentation is often influenced by factors like diverse organ shapes and blurred lesion boundaries in complex clinical scenarios. These issues result in subpar performance of current models in terms of maintaining structure and defining boundaries. To enhance the quality of segmentation, a three-dimensional medical image segmentation model with an encoder-decoder architecture is proposed. In the encoder, an axial gating module is first designed to extract key edge and structural information along spatial axes. A vision long short-term memory module is then introduced to model sequential feature dependencies in three-dimensional space and capture overall organ shape. Next, a selective memory module is designed to dynamically filter and store representative global features, retrieving relevant memory items to improve the representation of complex cases. In the decoder, a multi-scale aggregation module is designed to effectively fuse multi-scale features, thus further enhancing segmentation performance for complex anatomical structures. It compared the proposed method with four representative medical image segmentation models on the ACDC, Hippocampus and BraTS2021 datasets. The simulation results show that, compared to other methods, the proposed model more effectively preserves the overall morphology of target structures and achieves more precise boundary delineation.

Purpose:State space models have shown promise in medical image segmentation by modeling long-range dependencies with linear complexity. However, they are limited in their ability to capture local features, which hinders their capacity to extract multiscale details and integrate global and local contextual information effectively. To address these shortcomings, we propose the dual-path multi-scale Mamba UNet (DMM-UNet) model. Approach:This architecture facilitates deep fusion of local and global features through multi-scale modules within a U-shaped encoder-decoder framework. First, we introduce the multi-scale channel attention selective scanning block in the encoder, which combines global selective scanning with multi-scale channel attention to model both long-range and local dependencies simultaneously. Second, we design the spatial attention selective scanning block for the decoder. This block integrates global scanning with spatial attention mechanisms, enabling precise aggregation of semantic features through gated weighting. Finally, we develop the multi-dimensional collaborative attention layer to extract complementary attention weights across height, width, and channel dimensions, facilitating cross-space-channel feature interactions. Results:Experiments were conducted on the ISIC17, ISIC18, Synapse, and ACDC datasets. One of the indicators, Dice similarity coefficient, achieved 89.88% on the ISIC17 dataset, 90.52% on the ISIC18 dataset, 83.07% on the Synapse dataset, and 92.60% on the ACDC dataset. There are also other indicators that perform well on this model. Conclusions:The DMM-UNet model effectively addresses the shortcomings of state space models by enabling the integration of both local and global features, improving segmentation performance, and offering enhanced multiscale feature fusion for medical image segmentation tasks.

Underwater images collected are often of low clarity and suffer from severe color distortion due to the marine environment and Illumination conditions. This directly impacts tasks such as marine ecological monitoring and underwater target detection, which rely on image processing. Therefore, enhancing Underwater images to improve their quality is necessary. A generative adversarial network with an encoder-decoder structure is proposed to improve the quality of Underwater images. The network consists of a generative network and an adversarial network. The generative network is responsible for enhancing the images, while the adversarial network determines whether the input is an enhanced image or a real high-quality image. In the generative network, we first design a residual convolution module to extract more texture and edge information from underwater images. Next, we design a multi-scale dilated convolution module to capture underwater features at different scales. Then, we design a feature fusion adaptive attention module to reduce the interference of redundant features and enhance the local perception capabilities. Finally, we construct the generative network using these modules along with conventional modules. In the adversarial network, we first design a multi-scale feature extraction module to improve the feature extraction ability. We then use the multi-scale feature extraction module along with conventional convolution modules to design the adversarial network. Additionally, we propose an improved loss function by introducing color loss into the conventional loss function. The improved loss function can better measure the color discrepancy between the enhanced image and the real image. It is useful to reduce color distortion in the enhanced images. In experimental simulations, the images enhanced by the proposed methods have the highest PSNR, SSIM, and UIQM values, indicating that the proposed method has superior Underwater image enhancement capabilities compared to other methods.

Haze frequently deteriorates remote sensing images during the acquisition process, having a substantial impact on later studies. To handle this issue, a novel generative adversarial network with edge enhancement strategy is proposed, named Dehaze-EEGAN. The network is proposed to remove haze while preserving the edge and texture information in remote sensing images. A multi-scale feature extraction module is introduced to improve the capture of feature details from remote sensing images. This module enables the extraction of features across varying receptive field scales. Meanwhile, a novel attention module is designed. This module enhances the extraction of useful feature information by allocating larger weights to significant features. In addition, the discriminative ability of the adversarial network is enhanced by a designed multi-scale discriminative network. By introducing color loss and Charbonnier loss, the generative adversarial network's standard loss function is improved, which excels at retaining color details within remote sensing images while simultaneously mitigating the risk of gradient vanishing. By the use of simulated and real hazy remote sensing images as research subjects, the dehazing performance of the proposed method and four other methods are evaluated. According to the experimental results, the Dehaze-EEGAN method that had been suggested showed the greatest structural similarity (SSIM) and peak signal-to-noise ratio (PSNR), along with the smallest mean square error, learned perceptual image patch similarity, and natural image quality evaluator. Moreover, images dehazed using our proposed Dehaze-EEGAN method are more distinct than the outcomes from alternative methods.

Due to factors such as relative motion between the imaging device and the target object or out-of-focus optical system, some images may become blurred, severely affecting subsequent image processing tasks. We propose an image deblurring method based on deep convolutional neural networks to recover clear images from blurred ones. The network consists of three structurally similar sub-networks. Each sub-network comprises the multi-scale feature extraction module, single-scale feature extraction module, fast encoding-decoding module, encoding-decoding feature fusion module, and fusion strategy we proposed. The network can extract multi-scale features from input images of different scales, fusing coarse and fine-scale features and ultimately enhancing the clarity of the blurred images. In addition, we introduce the content loss function and frequency reconstruction loss function into the conventional loss function to better measure the quality of deblurred images. Experiments were conducted on the GoPro and HIDE datasets. The HIDE dataset is divided into distant and close-up scenes, allowing for a better evaluation of the generalization ability of deblurring methods in different scenarios. Compared with the existing methods, the experimental results demonstrate that the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index measure values, resulting in clearer and more natural deblurred images. Furthermore, the proposed algorithm exhibits faster image processing speed and smaller model size. The model performs better in terms of real-time capability.

Infrared and visible image fusion combines infrared and visible images of the same scene to produce a more informative and comprehensive fused image. Existing deep learning-based fusion methods fail to establish dependencies between global and local information during feature extraction. This results in unclear scene texture details and low contrast of the infrared thermal targets in the fused image. This paper proposes an infrared and visible image fusion network to address this issue via the use of a residual interactive transformer and cross-attention fusion. The network first introduces a residual dense module to extract shallow features from the input infrared and visible images. Next, the residual interactive transformer extracts global and local features from the source images and establishes interactions between them. Two identical residual interactive transformers are used for further feature extraction. A cross-attention fusion module is also designed to fuse the infrared and visible feature maps extracted by the residual interactive transformer. Finally, an image reconstruction network generates the fused image. The proposed method is evaluated on the RoadScene, TNO, and M3FD datasets. The experimental results show that the fused images produced by the proposed method contain more visible texture details and infrared thermal information. Compared to nine other methods, the proposed approach achieves superior fusion performance.

. Remote sensing images are often affected by atmospheric factors such as haze during the acquisition process, resulting in blurring and low contrast in the collected remote sensing images. This problem impacts the image quality, thereby affecting the analysis of remote sensing images. To mitigate the impact of haze on remote sensing images, a generative adversarial network is proposed. It comprises a generative network and an adversarial network. Firstly, a novel feature extraction module is designed to enhance the capability of extracting useful information from remote sensing images. It enables the network to focus more on regions with dense haze, allowing it to extract more important information while filtering out irrelevant details. Secondly, a residual attention module is designed which can allocate different weights based on varying haze density in the feature map. This module readjusts features outputted by the encoder, facilitating better image restoration. Thirdly, a multi-scale module is also incorporated to extract feature information across various image scales. Lastly, a color feature extraction module is designed to extract color features. The novel feature extraction module, residual attention module, multi-scale module, and color feature extraction module are utilized for constructing the generative network. Besides, an adversarial network is also designed to indirectly enhance the dehazing capability of the generative network. Synthetic and real datasets are used to test six different methods for dehazing remote sensing images, respectively. The proposed method achieves higher PSNR, SSIM, and lower MSE on the synthetic remote sensing dataset. On the other hand, it achieves lower PIQE, BRISQUE, and higher MetaIQA on the real remote sensing dataset. The proposed method has best performance in dehazing remote sensing images than other methods.

The scattering of tiny particles in the atmosphere causes a haze effect on remote sensing images captured by satellites and similar devices, significantly disrupting subsequent image recognition and classification. A generative adversarial network named TRPC-GAN with texture recovery and physical constraints is proposed to mitigate this impact. This network not only effectively removes haze but also better preserves the texture information of the original remote sensing image, thereby enhancing the visual quality of the dehazed image. A multi-scale module is proposed to extract feature information of remote sensing images, allowing it to capture image features from different receptive fields. Simultaneously, an attention module is designed further to guide the network’s focus towards important feature information. In addition, a multi-scale adversarial network is proposed to better restore both global and local information about the original image. Introducing a physical constraint loss function to improve the loss function of the original generative adversarial network allows for better preservation of the physical characteristics of remote sensing images. Simulation experiments on synthetic and natural hazy remote sensing image datasets are conducted. The results demonstrate that the dehazing performance of the TRPC-GAN method surpasses the other four methods.

Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet has been proposed to enhance both the detection speed and accuracy of defective photovoltaic modules. Firstly, a new bottleneck module is designed to replace the first bottleneck module of the last stage convolution group in the backbone. This new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules. Secondly, another bottleneck module is also designed and used to replace the original bottleneck module used in the fourth stage convolution group of the backbone. This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy. Besides, an improved intersection over union is proposed and introduced into the loss function to measure the difference between the predicted and ground truth boxes. This is useful for improving defect detection accuracy. Compared to other methods, the proposed method has the highest detection accuracy. Additionally, it also has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method.

Underwater environments pose significant challenges for image capture due to factors like light absorption, scattering, and the presence of particles in the water. These factors degrade the quality of underwater images, impacting tasks like target detection and recognition. The challenge with deep learning-based underwater image enhancement methods is their reliance on paired datasets, which consist of degraded and corresponding ground-truth images. Obtaining such paired datasets in natural conditions is challenging, leading to performance issues in these algorithms. To address this issue, we propose an unsupervised generative adversarial network with edge detection for enhancing underwater images without needing paired data. First, we introduce the perceptual loss function into the conventional loss function to better measure the performance of two generative networks. Second, we propose an edge extraction block based on the Laplacian operator, an attention module with an edge extraction block, a multi-scale feature module, a novel upsampling module, and a new downsampling module. We use these proposed modules to design a new generative network. Third, we use the proposed multi-scale feature and downsampling modules to design the adversarial network. We tested the algorithm’s performance on both synthetic and authentic underwater images. Compared to existing state-of-the-art methods, our proposed approach better enhances image details and restores color information.

Retinal fundus imaging is a crucial diagnostic tool in ophthalmology, enabling the early detection and monitoring of various ocular diseases. However, capturing high-resolution fundus images often presents challenges due to factors such as defocusing and diffraction in the digital imaging process, limited shutter speed, sensor unit density, and random noise in the image sensor or during image transmission. Super-resolution techniques offer a promising solution to overcome these limitations and enhance the visual details in retinal fundus images. Since the retina has rich texture details, the super-resolution images often introduce artifacts into texture details and lose some fine retinal vessel structures. To improve the perceptual quality of the retinal fundus image, a generative adversarial network that consists of a generator and a discriminator is proposed. The proposed generator mainly comprises 23 multi-scale feature extraction blocks, an image segmentation network, and 23 residual-in-residual dense blocks. These components are employed to extract features at different scales, acquire the retinal vessel grayscale image, and extract retinal vascular features, respectively. The generator has two branches that are mainly responsible for extracting global features and vascular features, respectively. The extracted features from the two branches are fused to better restore the super-resolution image. The proposed generator can restore more details and more accurate fine vessel structures in retinal images. The improved discriminator is proposed by introducing our designed attention modules to help the generator yield clearer super-resolution images. Additionally, an artifact loss function is also introduced to enhance the generative adversarial network, enabling more accurate measurement of the disparity between the high-resolution image and the restored image. Experimental results show that the generated images obtained by our proposed method have a better perceptual quality than the state-of-the-art image super-resolution methods.

Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two parts: a generative network and an adversarial network. The generative network performs image de-raining. The adversarial network determines whether the input image is rain-free or de-rained. The generative network comprises two branches: the A branch, which follows a traditional convolutional network structure, and the U branch, which utilizes a U-Net architecture. The A branch includes a multi-scale module for extracting information at different scales and a residual attention module to reduce redundant information interference. The U branch contains an encoder module designed to address the loss of details and local information caused by conventional down-sampling. To improve the performance of the generative network in image de-raining, this paper employs a relative discriminator incorporating a mean squared error loss. This discriminator better measures the differences between rainy and rain-free images while effectively preventing the occurrence of gradient vanishing. Finally, this study performs visual and quantitative comparisons of the proposed method and existing methods on three established rain image datasets. In the quantitative experiments, the proposed method outperforms existing methods regarding PSNR, SSIM, and VIF metrics. Specifically, our method achieves an average PSNR, SSIM, and VIF of approximately 5%, 3%, and 4% higher than the MFAA-GAN method, respectively. These results indicate that the de-rained images generated via the proposed method are closer to rain-free images.

During nighttime road scenes, images are often affected by contrast distortion, loss of detailed information, and a significant amount of noise. These factors can negatively impact the accuracy of segmentation and object detection in nighttime road scenes. A cycle-consistent generative adversarial network has been proposed to address this issue to improve the quality of nighttime road scene images. The network includes two generative networks with identical structures and two adversarial networks with identical structures. The generative network comprises an encoder network and a corresponding decoder network. A context feature extraction module is designed as the foundational element of the encoder-decoder network to capture more contextual semantic information with different receptive fields. A receptive field residual module is also designed to increase the receptive field in the encoder network.The illumination attention module is inserted between the encoder and decoder to transfer critical features extracted by the encoder to the decoder. The network also includes a multiscale discriminative network to discriminate better whether the image is a real high-quality or generated image. Additionally, an improved loss function is proposed to enhance the efficacy of image enhancement. Compared to state-of-the-art methods, the proposed approach achieves the highest performance in enhancing nighttime images, making them clearer and more natural.

In low-light environments, the amount of light captured by the camera sensor is reduced, resulting in lower image brightness. This makes it difficult to recognize or completely lose details in the image, which affects subsequent processing of low-light images. Low-light image enhancement methods can increase image brightness while better-restoring color and detail information. A generative adversarial network is proposed for low-quality image enhancement to improve the quality of low-light images. This network consists of a generative network and an adversarial network. In the generative network, a multi-scale feature extraction module, which consists of dilated convolutions, regular convolutions, max pooling, and average pooling, is designed. This module can extract low-light image features from multiple scales, thereby obtaining richer feature information. Secondly, an illumination attention module is designed to reduce the interference of redundant features. This module assigns greater weight to important illumination features, enabling the network to extract illumination features more effectively. Finally, an encoder-decoder generative network is designed. It uses the multi-scale feature extraction module, illumination attention module, and other conventional modules to enhance low-light images and improve quality. Regarding the adversarial network, a dual-discriminator structure is designed. This network has a global adversarial network and a local adversarial network. They determine if the input image is actual or generated from global and local features, enhancing the performance of the generator network. Additionally, an improved loss function is proposed by introducing color loss and perceptual loss into the conventional loss function. It can better measure the color loss between the generated image and a normally illuminated image, thus reducing color distortion during the enhancement process. The proposed method, along with other methods, is tested using both synthesized and real low-light images. Experimental results show that, compared to other methods, the images enhanced by the proposed method are closer to normally illuminated images for synthetic low-light images. For real low-light images, the images enhanced by the proposed method retain more details, are more apparent, and exhibit higher performance metrics. Overall, compared to other methods, the proposed method demonstrates better image enhancement capabilities for both synthetic and real low-light images.

Three basic issues of granular computing are construction or definition of granules, measures of granules, and computation or reasoning with granules. This paper reviews the main theories of granular computing and introduces the definition of spatial granules. A granule is composed of one or more atomic granules. The rationality of this definition is explained from the four aspects: simplicity, applicability, measurability and visualization. A one-to-one correspondence is established between the granules and the points in the unit hypercube, and the coarsening and refining of the granules are the descending and ascending dimensions of the points, respectively. The weak fuzzy tolerance relation and weak fuzzy equivalence relation are defined so as to study on all fuzzy binary relations. The notion of layer granularity/fineness is introduced and each granule can be easily denoted by two numbers, which can be used to pre-process macro knowledge space and greatly improve the search speed. This paper also discusses the main properties of granules including the necessary and sufficient conditions of coarse-fine relation and the main principles of granular space.

The images captured underwater are usually degraded due to the effects of light absorption and scattering. Degraded underwater images exhibit color distortion, low contrast, and blurred details, which in turn reduce the accuracy of marine biological monitoring and underwater object detection. To address this issue, a generative adversarial network with multi-scale and an attention mechanism is proposed to improve the quality of underwater images. To extract more effective features within the generative network, several modules are introduced: a multi-scale dilated convolution module, a novel attention module, and a residual module. These modules are utilized to design a generative network with a U-shaped structure. The multi-scale dilated convolution module is designed to extract features at multiple scales and expand the receptive field to capture more global information. The attention module directs the network’s focus towards important features, thereby reducing the interference from redundant feature information. To improve the discriminative power of the adversarial network, a multi-scale discriminator is designed. It has two output feature maps with different scales. Additionally, an improved loss function for the generative adversarial network is proposed. This improvement involves incorporating the total variation loss into the traditional loss function. The performance of different methods for enhancing underwater images is evaluated using the EUVP dataset and UIEB dataset. The experimental results demonstrate that the enhanced underwater images exhibit better quality and visual effects compared to other methods.

The degradation of visual quality in remote sensing images caused by haze presents significant challenges in interpreting and extracting essential information. To effectively mitigate the impact of haze on image quality, we propose an unsupervised generative adversarial network specifically designed for remote sensing image dehazing. This network includes two generators with identical structures and two discriminators with identical structures. One generator is focused on image dehazing, while the other generates images with added haze. The two discriminators are responsible for distinguishing whether an image is real or generated. The generator, employing an encoder–decoder architecture, is designed based on the proposed multi-scale feature-extraction modules and attention modules. The proposed multi-scale feature-extraction module, comprising three distinct branches, aims to extract features with varying receptive fields. Each branch comprises dilated convolutions and attention modules. The proposed attention module includes both channel and spatial attention components. It guides the feature-extraction network to emphasize haze and texture within the remote sensing image. For enhanced generator performance, a multi-scale discriminator is also designed with three branches. Furthermore, an improved loss function is introduced by incorporating color-constancy loss into the conventional loss framework. In comparison to state-of-the-art methods, the proposed approach achieves the highest peak signal-to-noise ratio and structural similarity index metrics. These results convincingly demonstrate the superior performance of the proposed method in effectively removing haze from remote sensing images.

为了帮助学生根据自身需求,利用碎片化时间进行"通信原理"课程的学习,文章设计了一个基于微信公众平台的"通信原理"课程线上教学平台,将学习内容分解成小块,以便学生能够在碎片化的时间段内进行学习.学生可以选择完成一个小模块的学习任务,而不需要连续长时间的学习.这种分段学习的方式不仅有助于学生更好地管理碎片化时间,提高学习效率,也解决了"通信原理"课程课堂学时有限、教学方法单一、师生课下交流不畅等教学现状.通过该教学平台的设计及应用,有效提高了师生之间的教学互动性,为大学生提供了崭新的学习平台和丰富多样的教学资源,有效地提高了"通信原理"课程的教学质量.

To improve the quality of the recovered image by the generative adversarial network, an improved generative adversarial network is proposed. Firstly, it designs a new batch normalization block to avoid gradient explosion and disappearance. The tra-ditional batch normalization will reduce the standard deviation of feature pixels, which causes degradation of reconstructed image quality. To solve the problem, an adaptive standard deviation of feature pixels modulator is designed to amplify the deviation of feature pixels and is introduced to traditional batch normalization to construct new batch normalization. Secondly, to extract more useful features, a new block is designed. The proposed block consists of two branches with different network depths. It fuses the dif-ferent extracted features from the two branches to obtain more useful features. Besides, the proposed batch normalization block also is introduced into the new block. Thirdly, the new block is used to construct a dense network with skip connection characteristics for extracting features. Besides, the new block is also used alone at the end of the feature extraction network to fuse different features. Compared with EnhanceNet, SRGAN, ES-RGAN, R-SRGAN, SAM+VAM on Set5, Set14, BSD200, and Urban100 datasets, our proposed method still has the greatest average PSNR, and SSIM for recovered images from the images downsampled the super-resolution images using a bicubic kernel with a scaling factor of x2, x3 and x4, respectively. The recovered image by our proposed method is closer to the ground-truth image than other methods.