CU-Mamba: Selective State Space Models with Channel Learning for Image
  Restoration
Rui Deng Tianpei Gu
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.