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Enhanced Deep Residual Networks for Single Image Super-Resolution
[article]
2017
arXiv
pre-print
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We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. ...
In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. ...
Introduction Image super-resolution (SR) problem, particularly single image super-resolution (SISR), has gained increasing research attention for decades. ...
arXiv:1707.02921v1
fatcat:64liy73w75fwfex5t24b5uwslm
Enhanced Deep Residual Networks for Single Image Super-Resolution
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. ...
In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. ...
Introduction Image super-resolution (SR) problem, particularly single image super-resolution (SISR), has gained increasing research attention for decades. ...
doi:10.1109/cvprw.2017.151
dblp:conf/cvpr/LimSKNL17
fatcat:qrrmnvwbhjfjnesmmsgxfcjhke
Fixing Acceleration and Image Resolution Issues of Nuclear Magnetic Resonance
2020
Symmetry
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It has been proven that the proposed algorithm can enhance image spatial resolution and reduce motion artefacts and scan times. ...
Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a deformable human body motion ...
network for image super-resolution [15] , Enhanced deep residual networks for single image super-resolution [14] and Image super-resolution using very deep residual channel attention networks [16] ...
doi:10.3390/sym12040681
fatcat:ymvqfkhxdvbwnp2veo5lijzk6a
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel single frame and multi-frame joint network (SMFN) for recovering high-resolution spherical videos from low-resolution inputs. ...
A novel loss function based on the weighted mean square error is proposed to emphasize on the super-resolution of the equatorial regions. ...
Single Image Super-Resolution Single image super-resolution methods aim at learning the mapping between low-resolution images and high-resolution images, they take a single low-resolution image as input ...
arXiv:2008.10320v1
fatcat:un7amaq52zfvhasxyned24jvv4
Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
2020
Algorithms
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as PET signal sensing with super-resolution (SR) image enhancement. ...
This paper presents a super-resolution image enhancement algorithm designed for handling highly sensitively compressed hybrid CT/PET scanners raw data. ...
I do not hold and I am not currently applying for any patents relating to the content of the manuscript. I do not have any other financial competing interests. ...
doi:10.3390/a13050129
fatcat:b2egb4selvfkhmdndbouowjbjm
Compnet: A New Scheme for Single Image Super Resolution Based on Deep Convolutional Neural Network
2018
IEEE Access
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In this paper, a novel residual deep network, called CompNet, is proposed for the single image super resolution problem without an excessive increase in the network complexity. ...
INDEX TERMS Image super resolution, residual learning, deep learning. ...
CONCLUSION In this work, a residual deep neural network architecture, called CompNet, has been proposed for the problem of single image super resolution. ...
doi:10.1109/access.2018.2874442
fatcat:wxfmykm43nctxfqvpdcdktm4ja
Towards super resolution in the compressed domain of learning-based image codecs
2021
Applications of Digital Image Processing XLIV
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Unlike the traditional approaches to image compression, learning-based codecs exploit deep neural networks for reducing dimensionality of the input at the stage where a linear transform would be typically ...
In this paper, we explore the possibilities and propose an approach for super resolution that is applied in the latent space. ...
•Enhanceddeep residual networks for single image super-resolution (EDSR)4by Lim et al. is a super resolution residual model, scoring first and second place at the NTIRE 2017 competition. ...
doi:10.1117/12.2597833
fatcat:xmp6ajpmdbb7to4pyn3bfd6eae
Image Super-Resolution Using Lightweight Multiscale Residual Dense Network
2020
International Journal of Optics
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In order to solve the problem, we propose a multiscale residual dense network (MRDN) for image super-resolution. This network is constructed based on the residual dense network. ...
Thus, it can better learn and fit the feature information of the original image and recover the satisfactory super-resolution image. ...
Kim et al. a deeply-recursive convolutional network (DRCN) [14] and a very deep convolutional network (VDSR) [25] for image super-resolution. ...
doi:10.1155/2020/2852865
fatcat:gyu5u2kfg5hdhf2ff3ewvcupny
Adaptive deep residual network for single image super-resolution
2019
Computational Visual Media
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In the single image super-resolution (SISR) task, the convolutional neural network (CNN) extracts the features of the image through deeper layers, and has achieved impressive results. ...
In this paper, we propose a single image super-resolution model based on Adaptive Deep Residual named as ADR-SR, which uses the Input Output Same Size (IOSS) structure, and releases the dependence of upsampling ...
Conclusions In summary, we proposes a single image superresolution model named ADR-SR based on adaptive deep residual, which can be used for super-resolution task with the same size of input and output ...
doi:10.1007/s41095-019-0158-8
fatcat:x3lkbu6qzrhdzknsfcpbi25eju
Balanced Two-Stage Residual Networks for Image Super-Resolution
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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In this paper, balanced two-stage residual networks (BT-SRN) are proposed for single image super-resolution. ...
The deep residual design with constrained depth achieves the optimal balance between the accuracy and the speed for super-resolving images. ...
networks for single image super-resolution. ...
doi:10.1109/cvprw.2017.154
dblp:conf/cvpr/FanSYLHYWWH17
fatcat:civz7z2ogvfbzn6cbrkfinjywa
Deep Convolutional Networks for Magnification of DICOM Brain Images
2019
International Journal of Innovative Computing, Information and Control
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In this paper, we propose a deep Convolutional Neural Network (CNN) for the enhancement of Digital Imaging and Communications in Medicine (DICOM) brain images. ...
Convolutional neural networks have recently achieved great success in Single Image Super-Resolution (SISR). SISR is the action of reconstructing a high-quality image from a low-resolution one. ...
The idea behind DenseNets is that
Deep Convolutional Networks for Magnification of DICOM Brain Images. We propose a deep convolutional network for the purpose of single image super-resolution. ...
doi:10.24507/ijicic.15.02.725
fatcat:drnh7rlx25chtmx3iv5t7ls36u
Super-resolution reconstruction of a digital elevation model based on a deep residual network
2020
Open Geosciences
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The results show that DEM super-resolution based on a deep residual network is better than that obtained using a neural network with fewer convolutional layers, and the reconstructed result of the DEM ...
based on a deep residual network is remarkably improved in terms of the peak signal to noise ratio and visual effect. ...
Acknowledgments: We would like to thank Wei He for providing important feedback, suggestions and code contributions to the project. ...
doi:10.1515/geo-2020-0207
fatcat:3el2jsdu4veiznw3mhrycmsafa
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 ...
Single-stage Residual Nets A single-stage design is composed of a single network; examples are shown next. • EDSR: The Enhanced Deep Super-Resolution (EDSR) [42] modifies the ResNet architecture [14 ...
arXiv:1904.07523v3
fatcat:ovihxjadfja55hrytvhggj5c6q
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
[article]
2020
arXiv
pre-print
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Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super resolution that is able to produce photorealistic images. ...
Despite the visual quality of these generated images, there is still room for improvement. In this fashion, the model is extended to further improve the perceptual quality of the images. ...
When only one low-resolution image is used, it is commonly called Single Image Super-Resolution (SISR). ...
arXiv:2001.08073v1
fatcat:d6klhqrmefbhteei3dgrqib3dm
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
[article]
2018
arXiv
pre-print
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We propose and train a single deep learning network that we term as SuRDCNN (super-resolution and denoising convolutional neural network), to perform these two tasks simultaneously . ...
Our model nearly replicates the architecture of existing state-of-the-art deep learning models for super-resolution and denoising. ...
In residual learning the network learns a mapping from the input image to noise. In our work, we develop a single network capable of performing image super-resolution and denoising. ...
arXiv:1809.08229v1
fatcat:5x7givz4offy3latk34wyrfy5u
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