Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques

TL;DR

This survey comprehensively reviews unsupervised video restoration and enhancement techniques, emphasizing their methodologies, challenges, and future research directions to improve visual quality and downstream task performance.

Contribution

It provides the first detailed categorization and analysis of unsupervised approaches in video restoration and enhancement, including loss functions and evaluation methods.

Findings

Unsupervised methods effectively address various video degradations.

Categorization of approaches reveals diverse strategies like domain translation and self-supervision.

Identification of key challenges guides future research directions.

Abstract

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.