ECMamba：结合Retinex引导的选择性状态空间模型用于高效多重曝光校正
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
AI总结：
针对现有曝光校正方法未充分嵌入Retinex理论、难平衡性能与效率的问题，提出双通路ECMamba框架，结合Retinex估计与Retinex引导的2D选择性状态空间层，实现高效高性能的多重曝光校正。
摘要：
曝光校正（Exposure Correction, EC）旨在为过曝或欠曝场景下拍摄的图像恢复合适的曝光条件。现有深度学习模型已展现出可观效果，但极少有模型将Retinex理论完全嵌入其架构中，这是当前方法体系存在的一处空白。此外，在曝光校正任务中，如何平衡高性能与高效性仍是一个尚未被充分探究的问题。受展现出强大且高效序列建模能力的Mamba启发，我们提出了一种基于Mamba的新型曝光校正框架ECMamba，该框架采用双通路结构，两条通路分别专门用于反射率图和光照图的恢复。具体而言，我们首先推导了Retinex理论，并训练了一个Retinex估计器，它能够将输入映射到两个中间空间，分别近似目标反射率图和光照图。这种设置为后续的曝光校正Mamba模块（Exposure Correction Mamba Module, ECMM）的精细化恢复过程提供了便利。此外，我们开发了一种由Retinex信息引导的新型二维选择性状态空间层（Retinex-SS2D），将其作为ECMM的核心算子。该架构融入了一种基于可变形特征聚合的创新二维扫描策略，从而同时提升了效率和效果。大量实验结果和全面的消融研究证明了我们提出的ECMamba的出色性能，以及其各组成部分的重要性。代码可在https://github.com/LowlevelAI/ECMamba获取。
英文摘要：
Exposure Correction (EC) aims to recover proper exposure conditions for images captured under over-exposure or under-exposure scenarios. While existing deep learning models have shown promising results, few have fully embedded Retinex theory into their architecture, highlighting a gap in current methodologies. Additionally, the balance between high performance and efficiency remains an under-explored problem for exposure correction task. Inspired by Mamba which demonstrates powerful and highly efficient sequence modeling, we introduce a novel framework based on Mamba for Exposure Correction (ECMamba) with dual pathways, each dedicated to the restoration of reflectance and illumination map, respectively. Specifically, we firstly derive the Retinex theory and we train a Retinex estimator capable of mapping inputs into two intermediary spaces, each approximating the target reflectance and illumination map, respectively. This setup facilitates the refined restoration process of the subsequent Exposure Correction Mamba Module (ECMM). Moreover, we develop a novel 2D Selective State-space layer guided by Retinex information (Retinex-SS2D) as the core operator of ECMM. This architecture incorporates an innovative 2D scanning strategy based on deformable feature aggregation, thereby enhancing both efficiency and effectiveness. Extensive experiment results and comprehensive ablation studies demonstrate the outstanding performance and the importance of each component of our proposed ECMamba. Code is available at https://github.com/LowlevelAI/ECMamba.
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。
