Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911050678403072 |
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| author | Malyugina, Alexandra Li, Yini Lin, Joanne Anantrasirichai, Nantheera |
| author_facet | Malyugina, Alexandra Li, Yini Lin, Joanne Anantrasirichai, Nantheera |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_08375 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques Malyugina, Alexandra Li, Yini Lin, Joanne Anantrasirichai, Nantheera Computer Vision and Pattern Recognition 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. |
| title | Unsupervised Methods for Video Quality Improvement: A Survey of Restoration and Enhancement Techniques |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.08375 |