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Single image deraining Using Dual Branch Network Based on Attention Mechanism for IoT

Computer Modeling in Engineering & Sciences 2023年第11期137卷 1989-2000页

作者： Di Wang Bingcai Wei Liye Zhang School of Computer Science and Technology Shandong University of TechnologyZibo255000China

Extracting useful details from images is essential for the Internet of Things ***,in real life,various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion,resulting in difficulties and obstacles to the extraction of key information,affecting the judgment of the real situation in the process of the Internet of Things,and causing system decision-making errors and *** this paper,we mainly solve the problem of rain on the image occlusion,remove the rain grain in the image,and get a clear image without ***,the single image deraining algorithm is studied,and a dual-branch network structure based on the attention module and convolutional neural network(CNN)module is proposed to accomplish the task of rain *** order to complete the rain removal of a single image with high quality,we apply the spatial attention module,channel attention module and CNN module to the network structure,and build the network using the coder-decoder *** the experiment,with the structural similarity(SSIM)and the peak signal-to-noise ratio(PSNR)as evaluation indexes,the training and testing results on the rain removal dataset show that the proposed structure has a good effect on the single image deraining task.

关键词： Internet of Things image deraining dual-branch network structure attention module convolutional neural network

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Unrolling a rain-guided detail recovery network for single-image deraining

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 11-23页

作者： Kailong LIN Shaowei ZHANG Yu LUO Jie LING School of Computer Science Guangdong University of TechnologyGuangzhou 510006China

Background Owing to the rapid development of deep networks, single-image deraining tasks have progressed significantly. Various architectures have been designed to recursively or directly remove rain, and most rain streaks can be removed using existing deraining methods. However, many of them cause detail loss, resulting in visual artifacts. Method To resolve this issue, we propose a novel unrolling rain-guided detail recovery network(URDRN) for single-image deraining based on the observation that the most degraded areas of a background image tend to be the most rain-corrupted regions. Furthermore, to address the problem that most existing deep-learningbased methods trivialize the observation model and simply learn end-to-end mapping, the proposed URDRN unrolls a single-image deraining task into two subproblems: rain extraction and detail recovery. Result Specifically, first, a context aggregation attention network is introduced to effectively extract rain streaks;thereafter, a rain attention map is generated as an indicator to guide the detail recovery process. For the detail recovery sub-network, with the guidance of the rain attention map, a simple encoder–decoder model is sufficient to recover the lost *** on several well-known benchmark datasets show that the proposed approach can achieve performance similar to those of other state-of-the-art methods.

关键词： image deraining Rain attention Detail recovery Unrolling network Context aggregation attention

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image deraining for UAV Using Split Attention Based Recursive Network

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Transactions of Nanjing University of Aeronautics and Astronautics 2020年第4期37卷 539-549页

作者： FENG Yidan DENG Sen WEI Mingqiang College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China

images captured in rainy days suffer from noticeable degradation of scene *** aerial vehicles(UAVs),as important outdoor image acquisition systems,demand a proper rain removal algorithm to improve visual perception quality of captured images as well as the performance of many subsequent computer vision *** deal with rain streaks of different sizes and directions,this paper proposes to employ convolutional kernels of different sizes in a multi-path *** attention is leveraged to enable communication across multiscale paths at feature level,which allows adaptive receptive field to tackle complex *** incorporate the multi-path convolution and the split attention operation into the basic residual block without increasing the channels of feature ***,every block in our network is unfolded four times to compress the network volume without sacrificing the deraining *** performance on various benchmark datasets demonstrates that our method outperforms state-of-the-art deraining algorithms in both numerical and qualitative comparisons.

关键词： unmanned aerial vehicle(UAV) deep neural network image deraining recursive computation split attention

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Joint Rain Streaks & Haze Removal Network for Object Detection

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Computers, Materials & Continua 2024年第6期79卷 4683-4702页

作者： Ragini Thatikonda Prakash Kodali Ramalingaswamy Cheruku Eswaramoorthy K.V Department of Electronics and Communication Engineering National Institute of TechnologyWarangal506004India Department of Computer Science and Engineering National Institute of TechnologyWarangal506004India Department of Electronics and Communication Engineering Indian Institute of Information TechnologyDesign and ManufacturingKurnool518008India

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant *** emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior ***,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational *** response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature *** methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map *** the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

关键词： image deraining Selective Dense Residual Module(SDRM) Selective Kernel Fusion Module(SKFM) Selective KernelDense Residual M-Shaped Network(SKDRMNet)

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image Rain Removal Using Conditional Generative Networks Incorporating

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Journal of Computer and Communications 2022年第2期10卷 72-82页

作者： Fangyan Zhang Xinzheng Xu Peng Wang School of Intelligent Engineering and Technology Ningxia University Yinchuan China

The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image processing. Most noise reduction methods more or less remove texture details in rain-free areas, resulting in an over-smoothing effect in the restored background. The research on image noise removal is very meaningful. We exploit the powerful generative power of a modified generative adversarial network (CGAN) by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean image. An efficient and lightweight attention machine mechanism NAM is introduced in the generator, and an IDN-CGAN model is proposed to capture image salient features through attention operations. Taking advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality, we also introduce a new fine-grained loss function in the generator-discriminator pair, predicting and real data degree of disparity to achieve improved results.

关键词： Attention Mechanism Conditional Production Adversarial Network Loss Function image deraining

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image Rain Removal Using Conditional Generative Networks Incorporating

Image Rain Removal Using Conditional Generative Networks Inc...

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作者： Fangyan Zhang Xinzheng Xu Peng Wang School of Intelligent Engineering and Technology Ningxia University

The research of removing rain from pictures or videos has always been an important topic in the field of computer vision and image *** noise reduction methods more or less remove texture details in rain-free areas,resulting in an over-smoothing effect in the restored *** research on image noise removal is very *** exploit the powerful generative power of a modified generative adversarial network（CGAN） by enforcing an additional condition that makes the derained image indistinguishable from its corresponding ground-truth clean *** efficient and lightweight attention machine mechanism NAM is introduced in the generator,and an IDN-CGAN model is proposed to capture image salient features through attention *** advantage of the mutual information in different dimensions of the features to further suppress insignificant channels or pixels to ensure better visual quality,we also introduce a new fine-grained loss function in the generator-discriminator pair,predicting and real data degree of disparity to achieve improved results.

关键词： Attention Mechanism Conditional Production Adversarial Network Loss Function image deraining

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