Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques
It addresses the problem of video quality improvement for applications in computer vision, but is incremental as it is a survey paper summarizing existing work.
This survey reviews unsupervised methods for video restoration and enhancement, focusing on techniques like domain translation and self-supervision to improve visual quality and boost downstream computer vision tasks, without providing specific numerical results.
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.