Edge-guided Low-light Image Enhancement with Inertial Bregman
  Alternating Linearized Minimization
Chaoyan Huang Zhongming Wu Tieyong Zeng
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.