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.
Bibliographical noteFunding Information:
Manuscript received November 4, 2019; revised December 30, 2019; accepted January 2, 2020. Date of publication January 10, 2020; date of current version February 12, 2020. This work was supported by the Research Fund of Chungnam National University. This work was performed when Dongbo Min was with Chungnam National University. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Daniel P. K. Lun. (Corresponding author: Dongbo Min.) H. Lee and K. Sohn are with the School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, South Korea (e-mail: firstname.lastname@example.org; email@example.com).
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics