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

• URL: http://arxiv.org/abs/2404.11778v1
• Date: Wed, 17 Apr 2024 22:02:22 GMT
• Title: CU-Mamba: Selective State Space Models with Channel Learning for Image Restoration
• Authors: Rui Deng, Tianpei Gu,
• Abstract summary: We introduce the Channel-Aware U-Shaped Mamba model, which incorporates a dual State Space Model framework into the U-Net architecture. Experiments validate CU-Mamba's superiority over existing state-of-the-art methods.
• Score: 7.292363114816646
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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