Low-light image enhancement (LLIE) aims to improve the visibility and perceptual quality of images captured under low-light conditions, enhancing as much structure and detail as the captured data allow. Existing methods often rely on deep neural networks to map low-light images to normal-light counterparts within color spaces such as standard red green blue (sRGB) or hue-saturation-value (HSV). However, these approaches are prone to noise interference and lack robustness, resulting in noticeable artifacts in color and brightness. To address these challenges, we propose task-decoupled framework for low-light image enhancement (TDNet), a task-decoupled LLIE framework that regards LLIE as a multi-task learning problem: denoising and color correction, enabling targeted optimization for each sub-task. Specifically, the denoising sub-task further decouples noise suppression across three channels and employs a fine-denoise channel attention module to effectively handle channel-specific noise heterogeneity. Simultaneously, a cross-channel fusion attention module captures global semantic information and inter-channel dependencies, enhancing color consistency and ensuring natural visual aesthetics. In addition, a progressive detail recovery strategy is incorporated to refine structural clarity and edge details. Extensive experiments on multiple benchmark datasets demonstrate that TDNet achieves favorable performance in noise suppression and color correction while maintaining low computational complexity and parameter efficiency (https://github.com/634949684aa/TDNet).
Liu et al. (Thu,) studied this question.