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A Review of Deep Learning Based Image Super-resolution Techniques

arXiv 2201.10521 ↗First posted 22 Jan 2022cs.CVcited by 0

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Abstract (arXiv)

Image super-resolution technology is the process of obtaining high-resolution images from one or more low-resolution images. With the development of deep learning, image super-resolution technology based on deep learning method is emerging. This paper reviews the research progress of the application of depth learning method in the field of image super-resolution, introduces this kind of super-resolution work from several aspects, and looks forward to the further application of depth learning method in the field of image super-resolution. By collecting and counting the relevant literature on the application of depth learning in the field of image super-resolution, we preliminarily summarizes the application results of depth learning method in the field of image super-resolution, and reports the latest progress of image super-resolution technology based on depth learning method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 85 references that are in the map. First posted on arXiv 22 Jan 2022.

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23 of 85 references.

References · 23

  1. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  2. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  3. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  4. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  5. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  6. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  7. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  8. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  9. 2018Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Networkcited by 263
  10. 2017MemNet: A Persistent Memory Network for Image Restorationcited by 244
  11. 2020Component Divide-and-Conquer for Real-World Image Super-Resolutioncited by 243
  12. 2019Residual Non-local Attention Networks for Image Restorationcited by 236
11 more
  1. 2016EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesiscited by 177
  2. 2019Feedback Network for Image Super-Resolutioncited by 155
  3. 2020Structure-Preserving Super Resolution with Gradient Guidancecited by 116
  4. 2019Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernelscited by 112
  5. 2020Invertible Image Rescalingcited by 92
  6. 2020Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolutioncited by 90
  7. 2021Flow-based Kernel Prior with Application to Blind Super-Resolutioncited by 74
  8. 2019Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Imagecited by 18
  9. 2019A New Local Transformation Module for Few-shot Segmentationcited by 15
  10. 2017A Perceptually Weighted Rank Correlation Indicator for Objective Image Quality Assessmentcited by 13
  11. 2019A New Deep Learning Method for Image Deblurring in Optical Microscopic Systemscited by 7

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