mlsanityData to 14 Oct 2026

Single Image Super-Resolution

arXiv 2101.02802 ↗First posted 8 Jan 2021eess.IV cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

This study presents a chronological overview of the single image super-resolution problem. We first define the problem thoroughly and mention some of the serious challenges. Then the problem formulation and the performance metrics are defined. We give an overview of the previous methods relying on reconstruction based solutions and then continue with the deep learning approaches. We pick 3 landmark architectures and present their results quantitatively. We see that the latest proposed network gives favorable output compared to the previous methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 8 Jan 2021.

Builds on

8 of 24 references.

References · 8

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  3. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  4. 2018Residual Dense Network for Image Super-Resolutioncited by 799
  5. 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
  6. 2018Deep Learning for Single Image Super-Resolution: A Brief Reviewcited by 158
  7. 2019Feedback Network for Image Super-Resolutioncited by 155
  8. 2019Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discriminationcited by 32

Built on by

Not cited yet.