# Single Image Super-Resolution Methods: A Survey

arXiv: https://arxiv.org/abs/2202.11763 · HTML: https://mlsanity.com/paper/2202.11763/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 17 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Failures of Gradient-Based Deep Learning](https://mlsanity.com/paper/1703.07950/) (2017)
- [Pixel Recursive Super Resolution](https://mlsanity.com/paper/1702.00783/) (2017)
- [MRI Super-Resolution with Ensemble Learning and Complementary Priors](https://mlsanity.com/paper/1907.03063/) (2019)
- [Structure-Preserving Image Super-resolution via Contextualized Multi-task Learning](https://mlsanity.com/paper/1707.08340/) (2017)
- [D-SRGAN: DEM Super-Resolution with Generative Adversarial Networks](https://mlsanity.com/paper/2004.04788/) (2020)
- [Perceptual Extreme Super Resolution Network with Receptive Field Block](https://mlsanity.com/paper/2005.12597/) (2020)
- [s-LWSR: Super Lightweight Super-Resolution Network](https://mlsanity.com/paper/1909.10774/) (2019)
- [Deep Residual Dense U-Net for Resolution Enhancement in Accelerated MRI Acquisition](https://mlsanity.com/paper/2001.04488/) (2020)
- [Single Image Super Resolution - When Model Adaptation Matters](https://mlsanity.com/paper/1703.10889/) (2017)
- [Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning](https://mlsanity.com/paper/1711.10312/) (2017)
- [A Deeply-Recursive Convolutional Network for Crowd Counting](https://mlsanity.com/paper/1805.05633/) (2018)
- [Self-Tuned Deep Super Resolution](https://mlsanity.com/paper/1504.05632/) (2015)

## Built on by (most cited first, 3 of 3)

- [Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement](https://mlsanity.com/paper/2211.09061/) (2022)
- [Frequency-Aware Vision Transformers for High-Fidelity Super-Resolution of Earth System Models](https://mlsanity.com/paper/2502.12427/) (2025)
- [Super Resolved Imaging with Adaptive Optics](https://mlsanity.com/paper/2508.04648/) (2025)

## 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.