# A Systematic Survey of Deep Learning-based Single-Image Super-Resolution

arXiv: https://arxiv.org/abs/2109.14335 · HTML: https://mlsanity.com/paper/2109.14335/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 118 of its 228 references that are in the map.
- First posted on arXiv 29 Sep 2021.

## Builds on (most cited first, 30 of 118)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)

## Built on by (most cited first, 19 of 19)

- [VRT: A Video Restoration Transformer](https://mlsanity.com/paper/2201.12288/) (2022)
- [Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer](https://mlsanity.com/paper/2204.13286/) (2022)
- [CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution](https://mlsanity.com/paper/2204.08696/) (2022)
- [Robust Unsupervised StyleGAN Image Restoration](https://mlsanity.com/paper/2302.06733/) (2023)
- [NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2204.09197/) (2022)
- [Dual-domain Modulation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2503.10047/) (2025)
- [FourierSR: A Fourier Token-based Plugin for Efficient Image Super-Resolution](https://mlsanity.com/paper/2503.10043/) (2025)
- [GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance](https://mlsanity.com/paper/2504.01722/) (2025)
- [Incorporating Transformer Designs into Convolutions for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2303.14324/) (2023)
- [Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement](https://mlsanity.com/paper/2211.09061/) (2022)
- [Toward Flare-Free Images: A Survey](https://mlsanity.com/paper/2310.14354/) (2023)
- [A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift](https://mlsanity.com/paper/2411.10231/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)
- [4KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation](https://mlsanity.com/paper/2605.24762/) (2026)
- [Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer](https://mlsanity.com/paper/2210.11019/) (2022)
- [Attention-Guided Multi-scale Interaction Network for Face Super-Resolution](https://mlsanity.com/paper/2409.00591/) (2024)
- [NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results](https://mlsanity.com/paper/2409.16947/) (2024)
- [Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding](https://mlsanity.com/paper/2505.19668/) (2025)
- [EatGAN: An Edge-Attention Guided Generative Adversarial Network for Single Image Super-Resolution](https://mlsanity.com/paper/2509.14550/) (2025)

## Abstract (arXiv)

Single-image super-resolution (SISR) is an important task in image processing, which aims to enhance the resolution of imaging systems. Recently, SISR has made a huge leap and has achieved promising results with the help of deep learning (DL). In this survey, we give an overview of DL-based SISR methods and group them according to their design targets. Specifically, we first introduce the problem definition, research background, and the significance of SISR. Secondly, we introduce some related works, including benchmark datasets, upsampling methods, optimization objectives, and image quality assessment methods. Thirdly, we provide a detailed investigation of SISR and give some domain-specific applications of it. Fourthly, we present the reconstruction results of some classic SISR methods to intuitively know their performance. Finally, we discuss some issues that still exist in SISR and summarize some new trends and future directions. This is an exhaustive survey of SISR, which can help researchers better understand SISR and inspire more exciting research in this field. An investigation project for SISR is provided at https://github.com/CV-JunchengLi/SISR-Survey.