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FAN: Frequency Aggregation Network for Real Image Super-resolution
[article]
2020
arXiv
pre-print
Preserved Fulltext
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]
2021
arXiv
pre-print
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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
2019
IEEE Access
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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]
2019
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2021
Mathematical Problems of Computer Science
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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]
2020
arXiv
pre-print
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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
2022
Electronics
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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]
2020
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2018
IEEE Access
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2110.15327v1 fatcat:ny56kdqttjgipfudjpagle5cre
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
2020
IEEE Access
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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]
2022
arXiv
pre-print
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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
2021
IET Computer Vision
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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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