Complete Corruption-Aware Retinex Framework for Low-Light Image Enhancement

Abstract

Retinex theory, which treats an image as a composition of illuminance and reflectance, has made significant progress in low-light image enhancement. Previous methods attempt to refine the impractical Retinex theory by introducing deviations in estimated illumination and reflectance to develop more practical and robust enhancement techniques. However, the fact that state-of-the-art approaches still produce inferior results suggests that some form of corruption may be over-looked. In this paper, we propose a novel Complete Corruption-Aware Retinex Framework (CCRF), which not only considers corruption in low-light imaging—such as high ISO or long exposure settings—but, more importantly, also accounts for corruption induced by the enhancement method itself. Guided by this framework, we propose a Robust Corruption-Aware Loss (RCL) that enables the model to be robust under extreme darkness and complex light-object interactions. Additionally, we propose a Light-Up Map Denoising (LMD) module, which further eliminates model-induced perturbations. With these two plug-and-play modules, downstream tasks (e.g., low-light object detection) can benefit significantly. Extensive experiments demonstrate that our methods can be seamlessly integrated into state-of-the-art approaches, resulting in significant performance improvements over these methods. Code will be available at github.com/eafi/ccrf.