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FAN: Frequency Aggregation Network for Real Image Super-resolution [article]

Yingxue Pang, Xin Li, Xin Jin, Yaojun Wu, Jianzhao Liu, Sen Liu, Zhibo Chen
2020 arXiv   pre-print
Single image super-resolution (SISR) aims to recover the high-resolution (HR) image from its low-resolution (LR) input image. With the development of deep learning, SISR has achieved great progress.  ...  Therefore, we propose FAN, a frequency aggregation network, to address the real-world image super-resolu-tion problem.  ...  Lan, R., Sun, L., Liu, Z., Lu, H., Su, Z., Pang, C., Luo, X.: Cascading and enhanced residual networks for accurate single-image super-resolution. IEEE Transactions on Cybernetics (2020) 21.  ... 
arXiv:2009.14547v1 fatcat:pmpzgexy2nchxcsop76mp6xt4i

In-Orbit Lunar Satellite Image Super Resolution for Selective Data Transmission [article]

Atal Tewari, Chennuri Prateek, Nitin Khanna
2021 arXiv   pre-print
We present a residual dense non-local attention network (RDNLA) that provides enhanced super-resolution outputs to improve the SR performance.  ...  As the resolution of images plays a critical role in making precise inferences, we also include in-orbit super-resolution (SR) in the system design.  ...  This task, commonly referred to as single image super-resolution (SISR), is an illposed problem since there are always multiple HR images corresponding to a single LR image [1] .  ... 
arXiv:2110.10109v1 fatcat:t3d7udh5onea5cevb7mno4j5s4

Multi-scale Residual Hierarchical Dense Networks for Single Image Super-Resolution

Chuangchuang Liu, Xianfang Sun, Changyou Chen, Paul L. Rosin, Yitong Yan, Longcun Jin, Xinyi Peng
2019 IEEE Access  
Single image super-resolution is known to be an ill-posed problem, which has been studied for decades.  ...  INDEX TERMS Convolutional neural networks, deep learning, multi-scale residual hierarchical dense, image super-resolution. 60572 2169-3536  ...  INTRODUCTION Single image super-resolution (SISR) aims to reconstruct a high-resolution (HR) image from its low-resolution (LR) version.  ... 
doi:10.1109/access.2019.2915943 fatcat:k37r3mccsjdy5dkoby2ntkghri

Deformable Non-local Network For Video Super-Resolution [article]

Hua Wang, Dewei Su, Longcun Jin, Chuangchuang Liu
2019 arXiv   pre-print
In this paper, we propose a novel deformable non-local network (DNLN) which is non-flow-based.  ...  To reconstruct the final high-quality HR video frames, we use residual in residual dense blocks to take full advantage of the hierarchical features.  ...  Related Work Single Image Super-resolution Dong et al.  ... 
arXiv:1909.10692v1 fatcat:kvmdpw3gpjhyvitfj7cjhc7e4e

Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution [article]

Wendi Xu, Ming Zhang
2018 arXiv   pre-print
As a step towards WARSHIP, our case study of image super resolution combines 3 components of RSH to deploy a CNN model of WARSHIP-XZNet, which performs a happy medium between speed and performance.  ...  To the best of our knowledge, we firstly summarize 5 more durable and complete deep learning components for vision, that is, WARSHIP.  ...  As a subset of image super resolution, single image super resolution (SISR, which is the topic of this paper and is interchangeable with image super resolution from now on.) is the process of inferring  ... 
arXiv:1810.01620v1 fatcat:u64vh7dfhza4jeux7k7sjjsx3i

Single Image Joint Motion Deblurring and Super-Resolution Using the Multi-Scale Channel Attention Modules

Misak T. Shoyan, National Polytechnic University of Armenia
2021 Mathematical Problems of Computer Science  
During the last decade, deep convolutional neural networks have significantly advanced the single image super-resolution techniques reconstructing realistic textural and spatial details.  ...  This work proposes a fully convolutional neural network to reconstruct high-resolution sharp images from the given motion blurry low-resolution images.  ...  As a super-resolution subnetwork, they develop a non-local residual network that contains RCABs [8] and self-attention-based [1] non-local blocks.  ... 
doi:10.51408/1963-0076 fatcat:ulnois4kvzggvclqywynfcfcfy

Single Image Super-Resolution via Residual Neuron Attention Networks [article]

Wenjie Ai, Xiaoguang Tu, Shilei Cheng, Mei Xie
2020 arXiv   pre-print
Deep Convolutional Neural Networks (DCNNs) have achieved impressive performance in Single Image Super-Resolution (SISR).  ...  In this paper, we propose a novel end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and effective SISR.  ...  CONCLUSION In this paper, we propose a Residual Neuron Attention Networks (RNAN) for high-realistic image super resolution.  ... 
arXiv:2005.10455v1 fatcat:afhzj42v7ncbtilldui6mruhey

A Dual CNN for Image Super-Resolution

Jiagang Song, Jingyu Xiao, Chunwei Tian, Yuxuan Hu, Lei You, Shichao Zhang
2022 Electronics  
In this paper, we propose a dual super-resolution CNN (DSRCNN) to obtain high-quality images.  ...  The proposed method is very suitable for complex scenes for image resolution. Experimental results show that the proposed DSRCNN is superior to other popular in SR networks.  ...  [4] combined non-local and local priors to achieve a non-local mean SR model with steering kernel regression. Zhang et al.  ... 
doi:10.3390/electronics11050757 fatcat:rixj2dxvnjf6nkzhyewiswiqvy

A Deep Journey into Super-resolution: A survey [article]

Saeed Anwar, Salman Khan, Nick Barnes
2020 arXiv   pre-print
single image super-resolution.  ...  We introduce a taxonomy for deep-learning based super-resolution networks that groups existing methods into nine categories including linear, residual, multi-branch, recursive, progressive, attention-based  ...  The loss used for training the SelNet is 2 . RCAN Residual Channel Attention Network (RCAN) [72] is a recently proposed deep CNN architecture for single image super-resolution.  ... 
arXiv:1904.07523v3 fatcat:ovihxjadfja55hrytvhggj5c6q

Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks [article]

Francesco Salvetti, Vittorio Mazzia, Aleem Khaliq, Marcello Chiaberge
2020 arXiv   pre-print
state-of-the-art for Multi-Image Super-Resolution for remote sensing applications.  ...  Convolutional Neural Networks (CNNs) have been consistently proved state-of-the-art results in image Super-Resolution (SR), representing an exceptional opportunity for the remote sensing field to extract  ...  By designing a dense residual network as the generative network in GAN, their method makes full use of the hierarchical features from low-resolution (LR) images. Dong et. al.  ... 
arXiv:2007.03107v2 fatcat:cdyx7nxxnnaxhczwoxggdsjdkm

Single Image Super-Resolution Using Dual-Branch Convolutional Neural Network

Xiaodong Gao, Ling Zhang, Xianglin Mou
2018 IEEE Access  
Recent advances in convolutional neural networks have demonstrated impressive reconstruction for single image super-resolution (SR).  ...  INDEX TERMS Convolutional neural network, single image super-resolution, dilated convolution, deconvolution, skip connection.  ...  According to the number of given LR images, the super-resolution can be classified into single image super-resolution (SISR) and multiimage super-resolution (MISR) [2] .  ... 
doi:10.1109/access.2018.2889760 fatcat:vle5j6x2wbcb5agngbyk5wqoym

MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution [article]

Chenyu You, Lianyi Han, Aosong Feng, Ruihan Zhao, Hui Tang, Wei Fan
2021 arXiv   pre-print
We introduce a non-local residual block, which enables each channel-wise feature to attend global spatial hierarchical features.  ...  To this end, we propose a novel one-stage memory enhanced graph attention network (MEGAN) for space-time video super-resolution.  ...  We also introduce the deep non-local residual learning to enable our model to adaptively learn mixed attention hierarchical features from multiple video sequences.  ... 
arXiv:2110.15327v2 fatcat:34ua4yrrvfdulcqzwnvuslybxe

Frequency Separation Network for Image Super-Resolution

Shanshan Li, Qiang Cai, Haisheng Li, Jian Cao, Lei Wang, Zhuangzi Li
2020 IEEE Access  
Specifically, we propose a novel Frequency Separation Network (FSN) for image super-resolution (SR).  ...  It is well-known that high-frequency information (e.g. textures, edges) is significant for single image super-resolution (SISR).  ...  Single Image Super-Resolution via Information Distillation Network (IDN) [8] , Learning Dual Convolutional Neural Networks for Low-Level Vision (Dual-CNN) [16] , Enhanced Deep Residual Networks for Single  ... 
doi:10.1109/access.2020.2972927 fatcat:yxqt7zw3f5gshbfgxulssngxai

Adaptive Cross-Layer Attention for Image Restoration [article]

Yancheng Wang, Ning Xu, Yingzhen Yang
2022 arXiv   pre-print
Extensive experiments on image restoration tasks, including single image super-resolution, image denoising, image demosaicing, and image compression artifacts reduction, validate the effectiveness and  ...  Non-local attention module has been proven to be crucial for image restoration.  ...  HNAS [36] also adopted a hierarchical search space for single image super-resolution. METHOD CLA: Cross-Layer Attention Non-Local Attention.  ... 
arXiv:2203.03619v2 fatcat:yy53rkfrmvepvcjnjbrtjsihxu

Parallax‐based second‐order mixed attention for stereo image super‐resolution

Chenyang Duan, Nanfeng Xiao
2021 IET Computer Vision  
Stereo image pairs can effectively enhance the performance of super-resolution (SR) since both intra-view and cross-view information can be used.  ...  To address this issue, in this work, a parallax-based second-order mixed attention stereo SR network (PSMASSRnet) is proposed to integrate the cross-view information from a stereo image pair for SR.  ...  for Image Super-resolution.  ... 
doi:10.1049/cvi2.12063 fatcat:mghofhx75zdu3cawc5wxlw3ufy
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