Reconstructing degraded images is a critical task in image processing.
Although CNN and Transformer-based models are prevalent in this field, they
exhibit inherent limitations, such as inadequate long-range dependency modeling
and high computational costs. To overcome these issues, we introduce the
Channel-Aware U-Shaped Mamba (CU-Mamba) model, which incorporates a dual State
Space Model (SSM) framework into the U-Net architecture. CU-Mamba employs a
Spatial SSM module for global context encoding and a Channel SSM component to
preserve channel correlation features, both in linear computational complexity
relative to the feature map size. Extensive experimental results validate
CU-Mamba's superiority over existing state-of-the-art methods, underscoring the
importance of integrating both spatial and channel contexts in image
restoration.

重建退化图像的关键任务在图像处理中非常重要，我们引入了基于通道感知的 U 形曼巴模型，它融合了双状态空间模型（SSM）框架与 U-Net 架构，通过引入空间 SSM 模块和通道 SSM 组件，它能够线性计算的复杂度相对于特征图大小进行全局上下文编码和保留通道相关特征，从而在图像恢复方面优于现有的最先进方法，凸显了整合空间和通道上下文的重要性。