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.

翻译：深度状态空间模型（SSMs），如近期的Mamba架构，正逐渐成为CNN和Transformer网络的有力替代方案。现有的基于Mamba的复原方法采用一种展平扫描策略，将图像块转换为1D序列后再进行扫描。然而，这种扫描范式忽略了局部像素依赖性，并通过将远处像素错误地置于相邻位置而引入空间错位，这降低了局部噪声感知能力，并在低级视觉任务中损害了图像清晰度。为克服这些问题，我们提出了一种新颖的切片扫描策略，该策略在切片内和切片间交替进行扫描。我们进一步设计了一种用于图像去模糊的新视觉状态空间模块（VSSM），并解决了当前基于Mamba的视觉模块的效率挑战。在此基础上，我们开发了XYScanNet，这是一种集成了轻量级特征融合模块的SSM架构，用于增强图像去模糊效果。XYScanNet在保持竞争力的失真度量同时，显著提升了感知性能。实验结果表明，与最接近的竞争者相比，XYScanNet将KID指标提升了$17\%$。我们的代码即将发布。