The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| _version_ | 1866918007599529984 |
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| author | Huang, Danni Zhu, Lingyu Lin, Zihao Zhu, Hanwei Wang, Shiqi Chen, Baoliang |
| author_facet | Huang, Danni Zhu, Lingyu Lin, Zihao Zhu, Hanwei Wang, Shiqi Chen, Baoliang |
| contents | There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches proposed to enhance low-light images, much less work has been dedicated to quality assessment and quality optimization of low-light enhancement. In this paper, to close the gap between enhancement and assessment, we propose a loop enhancement framework that produces a clear picture of how the enhancement of low-light images could be optimized towards better visual quality. In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures. The objective quality assessment measure plays a critical bridging role between visual quality and enhancement and is further incorporated in the optimization in learning the enhancement model towards perceptual optimally. Finally, we iteratively perform the enhancement and optimization tasks, enhancing the low-light images continuously. The superiority of the proposed scheme is validated based on various low-light scenes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_09738 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement Huang, Danni Zhu, Lingyu Lin, Zihao Zhu, Hanwei Wang, Shiqi Chen, Baoliang Image and Video Processing Computer Vision and Pattern Recognition There is an increasing consensus that the design and optimization of low light image enhancement methods need to be fully driven by perceptual quality. With numerous approaches proposed to enhance low-light images, much less work has been dedicated to quality assessment and quality optimization of low-light enhancement. In this paper, to close the gap between enhancement and assessment, we propose a loop enhancement framework that produces a clear picture of how the enhancement of low-light images could be optimized towards better visual quality. In particular, we create a large-scale database for QUality assessment Of The Enhanced LOw-Light Image (QUOTE-LOL), which serves as the foundation in studying and developing objective quality assessment measures. The objective quality assessment measure plays a critical bridging role between visual quality and enhancement and is further incorporated in the optimization in learning the enhancement model towards perceptual optimally. Finally, we iteratively perform the enhancement and optimization tasks, enhancing the low-light images continuously. The superiority of the proposed scheme is validated based on various low-light scenes. |
| title | The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2202.09738 |