CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration
<|reference_start|>CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration: 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.<|reference_end|>
@article{deng2024cu-mamba:,
    title={CU-Mamba: Selective State Space Models with Channel Learning for Image
  Restoration},
    author={Rui Deng, Tianpei Gu},
    journal={arXiv preprint arXiv:2404.11778},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.11778},
    primaryClass={cs.CV}
}