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Pyramid Separable Channel Attention Network for single image super-resolution

Computers, Materials & Continua 2024年第9期80卷 4687-4701页

作者： Congcong Ma Jiaqi Mi Wanlin Gao Sha Tao College of Information and Electrical Engineering China Agricultural UniversityBeijing100083China College of Artificial Intelligence Nankai UniversityTianjin300350China Key Laboratory of Agricultural Informatization Standardization Ministry of Agriculture and Rural AffairsChina Agricultural UniversityBeijing100083China

single image super-resolution(SISR)technology aims to reconstruct a clear,high-resolution image with more information from an input low-resolution image that is blurry and contains less *** technology has significant research value and is widely used in fields such as medical imaging,satellite image processing,and security *** significant progress in existing research,challenges remain in reconstructing clear and complex texture details,with issues such as edge blurring and artifacts still *** visual perception effect still needs further ***,this study proposes a Pyramid Separable Channel Attention Network(PSCAN)for the SISR *** designs a convolutional backbone network composed of Pyramid Separable Channel Attention blocks to effectively extract and fuse multi-scale *** expands the model’s receptive field,reduces resolution loss,and enhances the model’s ability to reconstruct texture ***,an innovative artifact loss function is designed to better distinguish between artifacts and real edge details,reducing artifacts in the reconstructed *** conducted comprehensive ablation and comparative experiments on the Arabidopsis root image dataset and several public *** experimental results show that the proposed PSCAN method achieves the best-known performance in both subjective visual effects and objective evaluation metrics,with improvements of 0.84 in Peak Signal-to-Noise Ratio(PSNR)and 0.017 in Structural Similarity Index(SSIM).This demonstrates that the method can effectively preserve high-frequency texture details,reduce artifacts,and have good generalization performance.

关键词： Deep learning single image super-resolution artifacts texture details

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Multi-perception large kernel convnet for efficient image super-resolution

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四川大学学报(自然科学版) 2025年第1期62卷 67-78页

作者： MIAO Xuan LI Zheng XU Wen-Zheng College of Computer Science(College of Software) Sichuan UniversityChengdu 610065China Tianfu Engineering-Oriented Numerical Simulation&Software Innovation Center Sichuan UniversityChengdu 610207China

Significant advancements have been achieved in the field of single image super-resolution(SISR)through the utilization of Convolutional Neural Networks(CNNs)to attain state-of-the-art *** efforts have explored the incorporation of Transformers to augment network performance in ***,the high computational cost of Transformers makes them less suitable for deployment on lightweight ***,the majority of enhancements for CNNs rely predominantly on small spatial convolutions,thereby neglecting the potential advantages of large kernel *** this paper,the authors propose a Multi-Perception Large Kernel convNet(MPLKN)which delves into the exploration of large kernel ***,the authors have architected a Multi-Perception Large Kernel(MPLK)module aimed at extracting multi-scale features and employ a stepwise feature fusion strategy to seamlessly integrate these *** addition,to enhance the network's capacity for nonlinear spatial information processing,the authors have designed a Spatial-Channel Gated Feed-forward Network(SCGFN)that is capable of adapting to feature interactions across both spatial and channel *** results demonstrate that MPLKN outperforms other lightweight image super-resolution models while maintaining a minimal number of parameters and FLOPs.

关键词： single image super-resolution Lightweight model Deep learning Large kernel

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Better Visual image super-resolution with Laplacian Pyramid of Generative Adversarial Networks

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Computers, Materials & Continua 2020年第9期64卷 1601-1614页

作者： Ming Zhao Xinhong Liu Xin Yao Kun He School of Computer Science and Engineering Central South UniversityChangsha410000China Apple Inc. California95014USA

Although there has been a great breakthrough in the accuracy and speed of super-resolution(SR)reconstruction of a single image by using a convolutional neural network,an important problem remains unresolved:how to restore finer texture details during image super-resolution reconstruction?This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network(ELSRGAN),based on the Laplacian pyramid to capture the high-frequency details of the *** combining Laplacian pyramids and generative adversarial networks,progressive reconstruction of super-resolution images can be made,making model applications more *** order to solve the problem of gradient disappearance,we introduce the Residual-in-Residual Dense Block(RRDB)as the basic network *** capacity benefits more from dense connections,is able to capture more visual features with better reconstruction effects,and removes BN layers to increase calculation speed and reduce calculation *** addition,a loss of content driven by perceived similarity is used instead of content loss driven by spatial similarity,thereby enhancing the visual effect of the super-resolution image,making it more consistent with human visual *** qualitative and quantitative evaluation of the baseline datasets shows that the proposed algorithm has higher mean-sort-score(MSS)than any state-of-the-art method and has better visual perception.

关键词： single image super-resolution generative adversarial networks Laplacian pyramid

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A Novel AlphaSRGAN for Underwater image super resolution

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Computers, Materials & Continua 2021年第11期69卷 1537-1552页

作者： Aswathy K.Cherian E.Poovammal SRM Institute of Science and Technology ChennaiIndia

Obtaining clear images of underwater scenes with descriptive details is an arduous *** imaging techniques fail to provide clear cut features and attributes that ultimately result in object recognition ***,a need for a system that produces clear images for underwater image study has been *** overcome problems in resolution and to make better use of the super-resolution(SR)method,this paper introduces a novel method that has been derived from the Alpha Generative Adversarial Network(AlphaGAN)model,named Alpha super resolution Generative Adversarial Network(AlphaSRGAN).The model put forth in this paper helps in enhancing the quality of underwater imagery and yields images with greater resolution and more concise *** undergo pre-processing before they are fed into a generator network that optimizes and reforms the structure of the network while enhancing the stability of the network that acts as the *** the images are processed by the generator network,they are passed through an adversarial method for training *** dataset used in this paper to learn single image super resolution(SISR)is the USR 248 *** supervision is performed by an unprejudiced function that simultaneously scrutinizes and improves the image *** of images is done with reference to factors like local style information,global content and *** dataset USR 248 which has a huge collection of images has been used for the study is composed of three collections of images—high(640×480)and low(80×60,160×120,and 320×240).Paired instances of different sizes—2×,4×and 8×—are also present in the *** like Mean Opinion Score(MOS),Peak Signal-to-Noise Ratio(PSNR),Structural Similarity(SSIM)and Underwater image Quality Measure(UIQM)scores have been compared to validate the improved efficiency of our model when compared to existing works.

关键词： Underwater imagery single image super-resolution perceptual quality generative adversarial network image super resolution

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for image super-resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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image super-resolution Based on Generative Adversarial Networks: A Brief Review

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Computers, Materials & Continua 2020年第9期64卷 1977-1997页

作者： Kui Fu Jiansheng Peng Hanxiao Zhang Xiaoliang Wang Frank Jiang School of Physics and Mechanical and Electronic Engineering Hechi UniversityYizhou546300China School of Electrical and Information Engineering Guangxi University of Science and TechnologyLiuzhou545006China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411100China School of Information Technology Deakin UniversityGeelongVIC 3220Australia

single image super resolution(SISR)is an important research content in the field of computer vision and image *** the rapid development of deep neural networks,different image super-resolution models have *** to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single *** addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual *** this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation ***,we review the improved network structures and loss functions of GAN-based perceptual ***,the advantages and disadvantages of different networks are analyzed by multiple comparative ***,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.

关键词： single image super-resolution generative adversarial networks deep learning computer vision

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SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

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Computers, Materials & Continua 2023年第2期74卷 2999-3014页

作者： Safi Ullah Seong-Ho Song Division of Software Hallym University1 Hallymdaehak-gilChuncheonGangwon-doKorea Department of Information Technology University of GujratJalalpur Jattan RoadGujratPakistan

Due to highly underdetermined nature of single image super-resolution(SISR)problem,deep learning neural networks are required to be more deeper to solve the problem *** of deep neural networks successful in the super-resolution(SR)problem is ResNet which can render the capability of deeper networks with the help of skip ***,zero padding(ZP)scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data *** this *** consider the ResNet with Partial Convolution based Padding(PCP)instead of ZP to solve SR *** training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities,patch image based SR performance is compared with single full image based *** experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.

关键词： single image super-resolution SRResNet patch inputs zero padding partial convolution based padding

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