A Deep Retinex-Based Low-Light Enhancement Network Fusing Rich Intrinsic Prior Information

Images captured under low-light conditions are characterized by lower visual quality and perception levels than images obtained in better lighting scenarios. Studies focused on low-light enhancement techniques seek to address this dilemma. However, simple image brightening results in significant noise, blurring, and color distortion. In this paper, we present a low-light enhancement (LLE) solution that effectively synergizes Retinex theory with deep learning. Specifically, we construct an efficient image gradient map estimation module based on convolutional networks that can efficiently generate noise-free image gradient maps to assist with denoising. Second, to improve upon the traditional optimization model, we design a matrix-preserving optimization method (MPOM) coupled with deep learning modules, and it exhibits high speed and low memory consumption. Third, we incorporate image structure, image texture, and implicit prior information to optimize the enhancement process for low-light conditions and overcome prevailing limitations, such as oversmoothing, significant noise, and so forth. Through extensive experiments, we show that our approach has notable advantages over the existing methods and demonstrate superiority and effectiveness, surpassing the state-of-the-art methods by an average of 1.23 dB in PSNR for the LOL and VE-LOL datasets. The code for the proposed method is available in a public repository for open-source use: https://github.com/luxunL/DRNet.