mlsanityData to 7 Oct 2026

Single Image Super-Resolution Methods: A Survey

arXiv 2202.11763 ↗First posted 17 Feb 2022eess.IV cs.CV cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Super-resolution (SR), the process of obtaining high-resolution images from one or more low-resolution observations of the same scene, has been a very popular topic of research in the last few decades in both signal processing and image processing areas. Due to the recent developments in Convolutional Neural Networks, the popularity of SR algorithms has skyrocketed as the barrier of entry has been lowered significantly. Recently, this popularity has spread into video processing areas to the lengths of developing SR models that work in real-time. In this paper, we compare different SR models that specialize in single image processing and will take a glance at how they evolved to take on many different objectives and shapes over the years.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 64 references that are in the map. First posted on arXiv 17 Feb 2022.

Builds on

21 of 64 references.

References · 21

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
  5. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,250
  6. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  7. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  8. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  9. 2015Deeply-Recursive Convolutional Network for Image Super-Resolutioncited by 317
  10. 2017Failures of Gradient-Based Deep Learningcited by 79
  11. 2017Pixel Recursive Super Resolutioncited by 44
  12. 2019MRI Super-Resolution with Ensemble Learning and Complementary Priorscited by 27
9 more
  1. 2017Structure-Preserving Image Super-resolution via Contextualized Multi-task Learningcited by 15
  2. 2020D-SRGAN: DEM Super-Resolution with Generative Adversarial Networkscited by 15
  3. 2020Perceptual Extreme Super Resolution Network with Receptive Field Blockcited by 15
  4. 2019s-LWSR: Super Lightweight Super-Resolution Networkcited by 11
  5. 2020Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisitioncited by 9
  6. 2017Single Image Super Resolution - When Model Adaptation Matterscited by 4
  7. 2017Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learningcited by 4
  8. 2018A Deeply-Recursive Convolutional Network for Crowd Countingcited by 2
  9. 2015Self-Tuned Deep Super Resolutioncited by 1

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 0; 2025: 2.

3 papers.

  1. 2022Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinementcited by 1
  2. 2025Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Modelscited by 1
  3. 2025Super Resolved Imaging with Adaptive Opticscited by 1