XYScanNet：一种用于单图像去模糊的状态空间模型
XYScanNet: A State Space Model for Single Image Deblurring
AI总结：
针对现有Mamba去模糊方法忽略局部依赖和空间错位问题，提出切片-扫描策略及VSSM模块，构建XYScanNet，在保持失真指标的同时显著提升感知质量，KID提升17%。
摘要：
深度状态空间模型（SSM），如最近的Mamba架构，正成为CNN和Transformer网络的有前景的替代方案。现有的基于Mamba的恢复方法通过利用一种展平-扫描策略来处理视觉数据，该策略在扫描之前将图像块转换为1D序列。然而，这种扫描范式忽略了局部像素依赖性，并通过将远距离像素错误地相邻放置而引入空间错位，这降低了局部噪声感知能力，并在低级视觉任务中降低了图像清晰度。为了克服这些问题，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扫描。我们进一步设计了一种新的用于图像去模糊的视觉状态空间模块（VSSM），并解决了当前基于Mamba的视觉模块的低效挑战。在此基础上，我们开发了XYScanNet，一种集成了轻量级特征融合模块的SSM架构，以增强图像去模糊效果。XYScanNet保持了有竞争力的失真指标，并显著提高了感知性能。实验结果表明，与最接近的竞争者相比，XYScanNet将KID提高了17%。
英文摘要：
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 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.
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