LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network

Understand

Transformer-based low-light enhancement methods have yielded promising performance by effectively capturing long-range dependencies in a global context.

• However, their elevated computational demand limits the scalability of multiple iterations in deep unfolding networks, and hence they have difficulty in flexibly balancing interpretability and distortion.
• To address this issue, we propose a novel Low-Light Enhancement method via relighting-guided Mamba with a deep unfolding network (LLEMamba), whose theoretical interpretability and fidelity are guaranteed by Retinex optimization and Mamba deep priors, respectively.
• Specifically, our LLEMamba first constructs a Retinex model with deep priors, embedding the iterative optimization process based on the Alternating Direction Method of Multipliers (ADMM) within a deep unfolding network.