Shedding light on images: Multi-level image brightness enhancement guided by arbitrary references. (November 2022)

• Record Type: Journal Article
• Authors: Wang, Ya'nan; Jiang, Zhuqing; Liu, Chang; Li, Kai; Men, Aidong; Wang, Haiying; Chen, Xiaobo
• Is Part Of: Pattern recognition. Volume 131(2022)
• Publication Date: 2022-11
• Subjects: Low-light image enhancement -- Multi-level mapping -- Arbitrary references -- Codec network -- Decomposition -- Concatenation; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108867
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A reference image as an interface presents users' aesthetics and guides enhancement. Decomposition and concatenation of image features achieve multi-level enhancement. The low coupling of feature components alleviates color distortion. Abstract: The non-linearity between human perception and image brightness levels results in different definitions of NORMAL-light. Thus, most existing low-light image enhancement methods which produce one-to-one mapping can not meet the aesthetic demand. Other pioneers enhance low-light images guided by a given value. However, the inherent problem of non-linearity will cause poor usability. To this end, we propose a user-friendly neural network for multi-level low-light image enhancement. Inspired by style transfer, our method decomposes an image into content component feature and luminance component feature in the latent space. Then we enhance the image brightness to different levels by concatenating the content components from low-light images and the luminance components from reference images. The network meets various user requirements by selecting different brightness references. Moreover, information except for brightness is preserved to alleviate color distortion. Extensive experiments demonstrate the superiority of our network against existing methods.