XYScanNet: An Interpretable State Space Model for Perceptual Image Deblurring

• URL: http://arxiv.org/abs/2412.10338v2
• Date: Sat, 01 Feb 2025 02:31:31 GMT
• Title: XYScanNet: An Interpretable State Space Model for Perceptual Image Deblurring
• Authors: Hanzhou Liu, Chengkai Liu, Jiacong Xu, Peng Jiang, Mi Lu,
• Abstract summary: Deep state-space models (SSMs) are emerging as a promising alternative to CNN and Transformer networks. We propose a novel slice-and-scan strategy that alternates scanning along intra-blur and inter-slices. We develop XYScanNet, an SSM architecture integrated with a lightweight feature fusion module for enhanced image deblurring.
• Score: 6.9752432140704705
• License:
• Abstract: Deep state-space models (SSMs), like recent Mamba architectures, are emerging as a promising alternative to CNN and Transformer networks. Existing Mamba-based restoration methods process the visual data by leveraging a flatten-and-scan strategy that converts image patches into a 1D sequence before scanning. However, this scanning paradigm ignores local pixel dependencies and introduces spatial misalignment by positioning distant pixels incorrectly adjacent, which reduces local noise-awareness and degrades image sharpness in low-level vision tasks. To overcome these issues, we propose a novel slice-and-scan strategy that alternates scanning along intra- and inter-slices. We further design a new Vision State Space Module (VSSM) for image deblurring, and tackle the inefficiency challenges of the current Mamba-based vision module. Building upon this, we develop XYScanNet, an SSM architecture integrated with a lightweight feature fusion module for enhanced image deblurring. XYScanNet, maintains competitive distortion metrics and significantly improves perceptual performance. Experimental results show that XYScanNet enhances KID by $17\%$ compared to the nearest competitor. Our code will be released soon.

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