全面梳理超分辨率领域150+方法，涵盖图像、视频等多场景。
Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects
- 按骨干结构对超分辨率方法分类，覆盖四大主流方向。
- 系统分析150余种单图、近70种视频超分方法及30种其他技术。
- 适合初学者入门和研究者快速定位关键技术与开放问题。
超分辨率（SR）因深度学习（DL）进展及对高质量视觉应用的需求，在计算机视觉领域备受关注。随着该领域扩展，大量综述相继出现，但多数局限于特定方向，缺乏整体性视角。本文深入回顾了单图像超分辨率（SISR）、视频超分辨率（VSR）、立体超分辨率（SSR）和光场超分辨率（LFSR）等多种方法，系统梳理了超过150种SISR方法、近70种VSR方法，以及约30种用于SSR和LFSR的技术。我们分析了各类方法的架构设计、数据集、评估协议、实验结果与计算复杂度，并基于不同目标构建了新的分类体系。同时探讨了当前仍被忽视的重要开放问题。本工作可为研究人员提供有价值的参考。为便于查阅，我们建立了专用仓库：https://github.com/AVC2-UESTC/Holistic-Super-Resolution-Review。
原文摘要 · Abstract (English)
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.
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