EWRD: Entropy-Weighted Low-Light Image Enhancement via Reverse Diffusion Model

Low-light enhancement significantly aids vision tasks under poor illumination conditions. Existing methods primarily focus on enhancing severely degraded low-light areas, improving illumination accuracy, or noise suppression, yet they often overlook the loss of additional details and color distortion during calculation. In this paper, we propose an innovative entropy-weighted low-light image enhancement method via the reverse diffusion model, aiming at addressing the limitations of the traditional Retinex decomposition model in preserving local pixel details and handling excessive smoothing issues. This method integrates an entropy-weighting mechanism for improved image quality and entropy, along with a reverse diffusion model to address the detail loss in total variation regularization and refine the enhancement process. Furthermore, we utilize long short-term memory networks for the learning reverse process and the simulation of image degradation, based on a thermodynamics-based nonlinear anisotropic diffusion model. Comparative experiments reveal the superiority of our method over conventional Retinex-based approaches in terms of detail preservation and visual quality. Extensive tests across diverse datasets demonstrate the exceptional performance of our method, evidencing its potential as a robust solution for low-light image enhancement.