Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization

• URL: http://arxiv.org/abs/2403.01142v1
• Date: Sat, 2 Mar 2024 09:00:57 GMT
• Title: Edge-guided Low-light Image Enhancement with Inertial Bregman Alternating Linearized Minimization
• Authors: Chaoyan Huang, Zhongming Wu, Tieyong Zeng
• Abstract summary: Prior-based methods for low-light image enhancement often face challenges in extracting available prior information from dim images. We introduce a simple yet effective Retinex model with the proposed edge extraction prior. This algorithm addresses the optimization problem associated with the edge-guided Retinex model, enabling effective enhancement of low-light images.
• Score: 21.335003558744496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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