综述无监督视频修复与增强技术，梳理主流方法与未来方向
Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques
- 按领域转换、自监督信号设计等思路分类无监督方法
- 强调合成数据集对客观评估的关键作用
- 适合关注视频质量提升的算法研究者参考
视频修复与增强不仅关乎视觉质量提升，更是诸多下游计算机视觉任务的重要预处理步骤。本文系统综述了视频修复与增强技术，尤其聚焦无监督方法。首先概述常见视频退化类型及其成因，继而回顾传统与深度学习方法的优劣。重点剖析无监督方法，按领域转换、自监督信号设计、盲区或噪声方法进行分类。同时梳理无监督视频修复中常用的损失函数，并讨论成对合成数据集在客观评估中的作用。最后指出当前关键挑战，展望未来研究方向。
原文摘要 · Abstract (English)
Video restoration and enhancement are critical not only for improving visual quality, but also as essential pre-processing steps to boost the performance of a wide range of downstream computer vision tasks. This survey presents a comprehensive review of video restoration and enhancement techniques with a particular focus on unsupervised approaches. We begin by outlining the most common video degradations and their underlying causes, followed by a review of early conventional and deep learning methods-based, highlighting their strengths and limitations. We then present an in-depth overview of unsupervised methods, categorise by their fundamental approaches, including domain translation, self-supervision signal design and blind spot or noise-based methods. We also provide a categorization of loss functions employed in unsupervised video restoration and enhancement, and discuss the role of paired synthetic datasets in enabling objective evaluation. Finally, we identify key challenges and outline promising directions for future research in this field.
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