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.

翻译：基于先验的低光图像增强方法常面临从暗淡图像中提取有效先验信息的挑战。为克服该局限，本文提出一种简洁高效的Retinex模型，并引入所设计的边缘提取先验。具体而言，我们设计了一个边缘提取网络，可直接从低光图像中捕获精细边缘特征。基于Retinex理论，我们将低光图像分解为光照分量和反射分量，并引入边缘引导的Retinex模型以实现低光图像增强。为求解该模型，我们提出了一种新型惯性Bregman交替线性化最小化算法。该算法解决了边缘引导Retinex模型相关的优化问题，从而有效增强低光图像。通过严格的理论分析，我们确定了该算法的收敛特性。此外，基于非凸优化理论，我们证明了所提算法收敛于问题的稳定点。最后，在多个真实低光图像数据集上的大量实验验证了所提方案的高效性与优越性。