Open AccessLow‐light image enhancement based on Retinex decomposition and adaptive gamma correction
Author(s) -
Yang Jingyu,
Xu Yuwei,
Yue Huanjing,
Jiang Zhongyu,
Li Kun
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12097
Subject(s) - color constancy , artificial intelligence , visibility , computer vision , computer science , pyramid (geometry) , image enhancement , decomposition , gamma correction , image (mathematics) , multiplication (music) , mathematics , optics , physics , ecology , combinatorics , biology
Low‐light images suffer from poor visibility and noise. In this paper, a low‐light image enhancement method based on Retinex decomposition is proposed. A pyramid network is first utilized to extract multi‐scale features to improve the quality of Retinex decomposition. Then the decomposed illumination is refined via an adaptive Gamma correction network to handle non‐uniform illumination, while the decomposed reflectance is refined with a lightweight network. Finally, the enhanced image is obtained by element‐wise multiplication between the refined illumination and reflectance components. Quantitative and qualitative experiments demonstrate the superiority of our method over state‐of‐the‐art image enhancement methods.