Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects
Not on the map. Why?
Abstract (arXiv)
Super-resolution (SR) has garnered significant attention within the computer vision community, driven by advances in deep learning (DL) techniques and the growing demand for high-quality visual applications. With the expansion of this field, numerous surveys have emerged. Most existing surveys focus on specific domains, lacking a comprehensive overview of this field. Here, we present an in-depth review of diverse SR methods, encompassing single image super-resolution (SISR), video super-resolution (VSR), stereo super-resolution (SSR), and light field super-resolution (LFSR). We extensively cover over 150 SISR methods, nearly 70 VSR approaches, and approximately 30 techniques for SSR and LFSR. We analyze methodologies, datasets, evaluation protocols, empirical results, and complexity. In addition, we conducted a taxonomy based on each backbone structure according to the diverse purposes. We also explore valuable yet under-studied open issues in the field. We believe that this work will serve as a valuable resource and offer guidance to researchers in this domain. To facilitate access to related work, we created a dedicated repository available at https://github.com/AVC2-UESTC/Holistic-Super-Resolution-Review.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 266 of its 436 references that are in the map. First posted on arXiv 19 Sep 2025.
Builds on
266 of 436 references.
References · 266
- 2017Attention Is All You Needcited by 60,361
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2017Mask R-CNNcited by 7,277
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2022Generative Adversarial Networkscited by 5,627
38 more
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 5,045
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
- 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,422
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
- 2015SSD: Single Shot MultiBox Detectorcited by 3,130
- 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
- 2021SwinIR: Image Restoration Using Swin Transformercited by 1,887
- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,778
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2021Image Super-Resolution via Iterative Refinementcited by 1,511
- 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,264
- 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
- 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
- 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
- 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,114
- 2021Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Datacited by 970
- 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 903
- 2020Pre-Trained Image Processing Transformercited by 893
- 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
- 2018Residual Dense Network for Image Super-Resolutioncited by 799
- 2015Cyclical Learning Rates for Training Neural Networkscited by 629
- 2017Video Enhancement with Task-Oriented Flowcited by 619
- 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 552
- 2021Designing a Practical Degradation Model for Deep Blind Image Super-Resolutioncited by 529
- 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 494
- 2023Exploiting Diffusion Prior for Real-World Image Super-Resolutioncited by 489
- 2017Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolutioncited by 453
- 2022Activating More Pixels in Image Super-Resolution Transformercited by 438
- 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
- 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 394
- 2020Local Implicit Grid Representations for 3D Scenescited by 373
- 2018Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transformcited by 371
- 2019Deep Learning for Image Super-resolution: A Surveycited by 369
- 2023ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shiftingcited by 365
- 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
50 most cited of 266 All 266 (JSON)
Built on by
Citing papers per year: 2025: 1.
1 paper.