Unsupervised low-light image enhancement by data augmentation and contrastive learning.
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| Title: | Unsupervised low-light image enhancement by data augmentation and contrastive learning. |
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| Authors: | Junzhe, Shao1 (AUTHOR), Zhibin, Zhang2 (AUTHOR) zhangzhibin@ict.ac.cn |
| Source: | Imaging Science Journal. May2025, Vol. 73 Issue 3, p354-362. 9p. |
| Subjects: | Image databases, Data augmentation, Visual perception, Image intensifiers, Data modeling |
| Abstract: | Today, with the increasing demand for visual perception and high-level computational vision tasks, the field of low-light enhancement is rapidly developing. However, models trained on existing datasets often fail or suffer significant performance degradation in real-world low-light scenarios. This performance degradation is frequently due to the limitations of current databases, which typically contain small quantities of paired images of a single type. This article proposes an unsupervised model with a unique data augmentation technique that transforms a regular image database into a paired image database. By adjusting image parameters during training to change exposure, a regular image database can be converted into a paired one. The model restores low-exposure images by extracting lighting features through comparative learning. Evaluations of the LOL and DIV2K datasets demonstrate the proposed model's effectiveness, achieving notable results in low-light image enhancement. This method removes dataset restrictions, broadening the model's range of applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Today, with the increasing demand for visual perception and high-level computational vision tasks, the field of low-light enhancement is rapidly developing. However, models trained on existing datasets often fail or suffer significant performance degradation in real-world low-light scenarios. This performance degradation is frequently due to the limitations of current databases, which typically contain small quantities of paired images of a single type. This article proposes an unsupervised model with a unique data augmentation technique that transforms a regular image database into a paired image database. By adjusting image parameters during training to change exposure, a regular image database can be converted into a paired one. The model restores low-exposure images by extracting lighting features through comparative learning. Evaluations of the LOL and DIV2K datasets demonstrate the proposed model's effectiveness, achieving notable results in low-light image enhancement. This method removes dataset restrictions, broadening the model's range of applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13682199 |
| DOI: | 10.1080/13682199.2024.2395751 |
