  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. Unlike Transformer, to assist the deep unfolding framework with
multiple iterations, the proposed LLEMamba introduces a novel Mamba
architecture with lower computational complexity, which not only achieves
light-dependent global visual context for dark images during reflectance
relight but also optimizes to obtain more stable closed-form solutions.
Experiments on the benchmarks show that LLEMamba achieves superior quantitative
evaluations and lower distortion visual results compared to existing
state-of-the-art methods.
