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CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration

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arxiv 2404.11778 v1 pith:DBCKKLAZ submitted 2024-04-17 cs.CV

Rui Deng , Tianpei Gu This is my paper · ORCID
classification cs.CV
keywords channelcu-mambaimagecomputationalmodelmodelsrestorationspace
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration

    cs.CV 2025-06 conditional novelty 5.0 of 10

    M2Restore is a CLIP-guided Mixture-of-Experts Mamba-CNN model that reports state-of-the-art results on the All-weather all-in-one image restoration benchmark.

  2. CMamba: Learned Image Compression with State Space Models

    eess.IV 2025-02 conditional novelty 5.0 of 10

    A hybrid CNN and Mamba (state space model) image compression codec reports BD-Rate savings of 14.95% to 18.83% over VVC with fewer parameters, FLOPs, and lower decoding time than the prior best learned method.

  3. MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration

    cs.CV 2024-12 conditional novelty 5.0 of 10

    MaIR combines a stripe-based S-shaped scanning strategy and a sequence-shuffle attention block to improve Mamba-based image restoration, reporting new best PSNR on 14 benchmarks.

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