A switched view of Retinex: Deep self-regularized low-light image enhancement

Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains of paired or unpaired training data that are time-consuming to obtain. However, existing methods suffer color deviation and fail to generalize to various lighting conditions. This paper presents a novel self-regularized method based on Retinex, which, inspired by HSV, preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value). Besides, we design a novel random brightness disturbance approach to generate another abnormal brightness of the same scene. It is combined with the original form of brightness to estimate the same reflectance, which is achieved by a CNN. The reflectance, which is assumed irrelevant to any illumination according to the Retinex theory, is treated as the enhanced brightness. Our method is efficient as a low-light image is decoupled into two subspaces, i.e., color and brightness, for better preservation and enhancement. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively and adapts to more lighting conditions. Our code is available at https://github.com/Github-LHT/A-Switched-View-of-Retinex-Deep-Self-Regularized-Low-Light-ImageEnhancement. (c) 2021 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).