Colorization-Inspired Customized Low-Light Image Enhancement by a Decoupled Network

Recently, numerous inspirational approaches have been proposed to enhance the visual quality of the images captured under poor lighting conditions. Simultaneously, in order to accommodate diverse user esthetics, researchers have explored customized operations within the enhancement process. However, most existing studies ignore the significance of the chrominance component, which often leads to unsatisfactory results in terms of color. To address this issue, we novelly decompose the low-light image enhancement (LLIE) task into the brightening and colorization subtasks and develop a decoupled network called CCNet for colorization-inspired customized enhancement. Specifically, the brightening subtask aims to restore images with normal contrast, less noise, and sharper details. While the colorization subtask utilizes the chrominance information from low-light images as color guidance to predict rich chrominance in enhanced images. Then, in the inference stage, users can adjust the color style or the saturation of color guidance to obtain customized results. Extensive experiments demonstrate that our proposed method achieves superior performance in both general and customized LLIE tasks—particularly in terms of improving chrominance components. Code is available at: https://github.com/FVL2020/CCNet.