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Unsupervised Low-Light Image Enhancement Using Bright Channel Prior
- Title
- Unsupervised Low-Light Image Enhancement Using Bright Channel Prior
- Authors
- Lee, Hunsang; Sohn, Kwanghoon; Min, Dongbo
- Ewha Authors
- 민동보
- SCOPUS Author ID
- 민동보
![scopus](/images/layout/icon2.png)
- Issue Date
- 2020
- Journal Title
- IEEE SIGNAL PROCESSING LETTERS
- ISSN
- 1070-9908
1558-2361
- Citation
- IEEE SIGNAL PROCESSING LETTERS vol. 27, pp. 251 - 255
- Keywords
- Unsupervised learning; low-light image enhancement; bright channel prior
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Indexed
- SCIE; SCOPUS
![WOS](/images/layout/wos.gif)
- Document Type
- Article
- Abstract
- Recent approaches for low-light image enhancement achieve excellent performance through supervised learning based on convolutional neural networks. However, it is still challenging to collect a large amount of low-/normal-light image pairs in real environments for training the networks. In this letter, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP) that the brightest pixel in a small patch is likely to be close to 1. An unsupervised loss function is defined with the pseudo ground-truth generated using the BCP. An enhancement network, consisting of a simple encoder-decoder, is then trained using the unsupervised loss function. To the best of our knowledge, this is the first attempt that enhances a low-light image through unsupervised learning. Furthermore, we introduce saturation loss and self-attention map for preserving image details and naturalness in the enhanced result. The performance of the proposed method is validated on various public datasets. Experimental results demonstrate that the proposed unsupervised approach achieves competitive performance over state-of-the-art methods based on supervised learning.
- DOI
- 10.1109/LSP.2020.2965824
- Appears in Collections:
- 인공지능대학 > 컴퓨터공학과 > Journal papers
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