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http://dx.doi.org/10.9717/kmms.2020.23.2.135

Unsupervised Learning with Natural Low-light Image Enhancement  

Lee, Hunsang (School of Electrical & Electronic Engineering, Yonsei University)
Sohn, Kwanghoon (School of Electrical & Electronic Engineering, Yonsei University)
Min, Dongbo (Department of Computer Science & Engineering, Ewha Womans University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.2, 2020 , pp. 135-145 More about this Journal
Abstract
Recently, deep-learning based methods for low-light image enhancement accomplish great success through supervised learning. However, they still suffer from the lack of sufficient training data due to difficulty of obtaining a large amount of low-/normal-light image pairs in real environments. In this paper, we propose an unsupervised learning approach for single low-light image enhancement using the bright channel prior (BCP), which gives the constraint that the brightest pixel in a small patch is likely to be close to 1. With this prior, pseudo ground-truth is first generated to establish an unsupervised loss function. The proposed enhancement network is then trained using the proposed unsupervised loss function. To the best of our knowledge, this is the first attempt that performs a low-light image enhancement through unsupervised learning. In addition, we introduce a self-attention map for preserving image details and naturalness in the enhanced result. We validate the proposed method on various public datasets, demonstrating that our method achieves competitive performance over state-of-the-arts.
Keywords
Unsupervised Learning; Low-light Enhancement; Bright Channel Prior;
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