Prior-based methods for low-light image enhancement often face challenges in
extracting available prior information from dim images. To overcome this
limitation, we introduce a simple yet effective Retinex model with the proposed
edge extraction prior. More specifically, we design an edge extraction network
to capture the fine edge features from the low-light image directly. Building
upon the Retinex theory, we decompose the low-light image into its illumination
and reflectance components and introduce an edge-guided Retinex model for
enhancing low-light images. To solve the proposed model, we propose a novel
inertial Bregman alternating linearized minimization algorithm. This algorithm
addresses the optimization problem associated with the edge-guided Retinex
model, enabling effective enhancement of low-light images. Through rigorous
theoretical analysis, we establish the convergence properties of the algorithm.
Besides, we prove that the proposed algorithm converges to a stationary point
of the problem through nonconvex optimization theory. Furthermore, extensive
experiments are conducted on multiple real-world low-light image datasets to
demonstrate the efficiency and superiority of the proposed scheme.

为了克服在提取暗图像中可用的先验信息方面所面临的挑战，本文提出了一种简单而有效的具有边缘提取先验的 Retinex 模型。通过设计一个边缘提取网络，直接从低光图像中捕捉精细的边缘特征，将低光图像分解为其照明和反射分量，并引入边缘引导的 Retinex 模型用于增强低光图像，为了解决所提出的模型，我们提出了一种新颖的惯性 Bregman 交替线性最小化算法。通过严格的理论分析，我们建立了算法的收敛性质。此外，通过对多个真实世界的低光图像数据集进行广泛实验，证明了所提出方案的有效性和优越性。