# Hitchhiker's Guide to Super-Resolution: Introduction and Recent Advances

arXiv: https://arxiv.org/abs/2209.13131 · HTML: https://mlsanity.com/paper/2209.13131/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 112 of its 191 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 30 of 112)

- [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)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [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)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (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)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [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)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [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)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)

## Built on by (most cited first, 22 of 22)

- [Deepfake Generation and Detection: A Benchmark and Survey](https://mlsanity.com/paper/2403.17881/) (2024)
- [Diffusion Models, Image Super-Resolution And Everything: A Survey](https://mlsanity.com/paper/2401.00736/) (2024)
- [Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution](https://mlsanity.com/paper/2304.01994/) (2023)
- [SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning](https://mlsanity.com/paper/2306.14070/) (2023)
- [Dynamic Attention-Guided Diffusion for Image Super-Resolution](https://mlsanity.com/paper/2308.07977/) (2023)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network](https://mlsanity.com/paper/2305.02126/) (2023)
- [A Study in Dataset Pruning for Image Super-Resolution](https://mlsanity.com/paper/2403.17083/) (2024)
- [DWA: Differential Wavelet Amplifier for Image Super-Resolution](https://mlsanity.com/paper/2307.04593/) (2023)
- [Multi-Depth Branch Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2309.17334/) (2023)
- [UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2410.20348/) (2024)
- [Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities](https://mlsanity.com/paper/2410.08534/) (2024)
- [GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance](https://mlsanity.com/paper/2504.01722/) (2025)
- [Federated Learning for Blind Image Super-Resolution](https://mlsanity.com/paper/2404.17670/) (2024)
- [Webcam-based Pupil Diameter Prediction Benefits from Upscaling](https://mlsanity.com/paper/2408.10397/) (2024)
- [A Study in Dataset Distillation for Image Super-Resolution](https://mlsanity.com/paper/2502.03656/) (2025)
- [Guided Unconditional and Conditional Generative Models for Super-Resolution and Inference of Quasi-Geostrophic Turbulence](https://mlsanity.com/paper/2507.00719/) (2025)
- [A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift](https://mlsanity.com/paper/2411.10231/) (2024)
- [Embedding Similarity Guided License Plate Super Resolution](https://mlsanity.com/paper/2501.01483/) (2025)
- [Implicit Neural Representation-Based Continuous Single Image Super-Resolution: An Empirical Benchmark](https://mlsanity.com/paper/2601.17723/) (2026)
- [Enhanced Self-Supervised Multi-Image Super-Resolution for Camera Array Images](https://mlsanity.com/paper/2604.06816/) (2026)
- [MOSAIC: Multi-Scale Object-Centric Slot Adaptation with Image-Guided Prototype Conditioning for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2610.13547/) (2026)

## Abstract (arXiv)

With the advent of Deep Learning (DL), Super-Resolution (SR) has also become a thriving research area. However, despite promising results, the field still faces challenges that require further research e.g., allowing flexible upsampling, more effective loss functions, and better evaluation metrics. We review the domain of SR in light of recent advances, and examine state-of-the-art models such as diffusion (DDPM) and transformer-based SR models. We present a critical discussion on contemporary strategies used in SR, and identify promising yet unexplored research directions. We complement previous surveys by incorporating the latest developments in the field such as uncertainty-driven losses, wavelet networks, neural architecture search, novel normalization methods, and the latests evaluation techniques. We also include several visualizations for the models and methods throughout each chapter in order to facilitate a global understanding of the trends in the field. This review is ultimately aimed at helping researchers to push the boundaries of DL applied to SR.