Retinexmamba: 基于Retinex的Mamba低光照图像增强方法
Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement
AI总结：
针对现有低光照图像增强方法的缺陷，提出结合传统Retinex物理特性与深度学习框架、引入SSM与融合注意力的RetinexMamba架构，在LOL数据集上表现优于现有同类方法。
摘要：
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各有显著优势与局限。传统Retinex方法旨在模拟人眼对亮度与色彩的感知，将图像分解为光照分量与反射分量，但在低光照条件下难以处理噪声并保留细节。Retinexformer通过传统自注意力机制改进光照估计，但存在可解释性不足、增强效果欠佳的问题。为克服这些局限，本文提出RetinexMamba架构。RetinexMamba既保留了传统Retinex方法的物理直观性，又整合了Retinexformer的深度学习框架，借助状态空间模型（SSMs）的计算效率提升处理速度。该架构具备创新的光照估计器与损伤修复机制，可在增强过程中维持图像质量。此外，RetinexMamba用融合注意力（Fused-Attention）机制替代Retinexformer中的光照引导多头注意力（IG-MSA），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，证实了其在低光照图像增强中的有效性与优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
