In the field of low-light image enhancement, both traditional Retinex methods and advanced deep learning techniques such as Retinexformer have shown distinct advantages and limitations. Traditional Retinex methods, designed to mimic the human eye's perception of brightness and color, decompose images into illumination and reflection components but struggle with noise management and detail preservation under low light conditions. Retinexformer enhances illumination estimation through traditional self-attention mechanisms, but faces challenges with insufficient interpretability and suboptimal enhancement effects. To overcome these limitations, this paper introduces the RetinexMamba architecture. RetinexMamba not only captures the physical intuitiveness of traditional Retinex methods but also integrates the deep learning framework of Retinexformer, leveraging the computational efficiency of State Space Models (SSMs) to enhance processing speed. This architecture features innovative illumination estimators and damage restorer mechanisms that maintain image quality during enhancement. Moreover, RetinexMamba replaces the IG-MSA (Illumination-Guided Multi-Head Attention) in Retinexformer with a Fused-Attention mechanism, improving the model's interpretability. Experimental evaluations on the LOL dataset show that RetinexMamba outperforms existing deep learning approaches based on Retinex theory in both quantitative and qualitative metrics, confirming its effectiveness and superiority in enhancing low-light images.

翻译：在低光照图像增强领域，传统Retinex方法与Retinexformer等先进深度学习技术均展现出各自的优势与局限性。传统Retinex方法通过模拟人眼对亮度与色彩的感知机制，将图像分解为照明分量和反射分量，但在低光照条件下存在噪声抑制与细节保持方面的不足。Retinexformer通过传统自注意力机制优化照明估计，但其可解释性不足且增强效果欠佳。为突破上述局限，本文提出RetinexMamba架构。该架构既保留了传统Retinex方法的物理直观性，又融合了Retinexformer的深度学习框架，并借助状态空间模型（State Space Models, SSMs）的计算效率提升处理速度。其创新设计的照明估计器与损伤修复器可在增强过程中维持图像质量。此外，RetinexMamba采用融合注意力（Fused-Attention）机制替代Retinexformer中的IG-MSA（照明引导多头注意力），显著提升了模型的可解释性。在LOL数据集上的实验评估表明，RetinexMamba在定量与定性指标上均优于现有基于Retinex理论的深度学习方法，验证了其在低光照图像增强中的有效性与优越性。