English(EN) XYScanNet: A State Space Model for Single Image Deblurring

新的状态空间模型架构增强图像去模糊效果

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-06 05:00

研究人员推出了一种用于单图像去模糊的新型架构XYScanNet，该架构采用了状态空间模型（SSM）方法。与将图像块转换为一维序列的先前基于Mamba的方法不同，XYScanNet采用“切片-扫描”策略，以更好地保留局部像素依赖性并提高清晰度。这种名为视觉状态空间模块（VSSM）的新模块解决了当前Mamba视觉模型的效率挑战，并已在失真指标和感知性能方面展现出竞争力，在KID指标上超越竞争对手17%。 AI

影响引入了一种使用状态空间模型进行图像去模糊的新颖方法，有望提高低级视觉任务的性能。

排序理由该集群包含一篇详细介绍特定计算机视觉任务新模型架构的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.CV TIER_1 English(EN) · Hanzhou Liu, Chengkai Liu, Jiacong Xu, Peng Jiang, Mi Lu · 2026-11-06 05:00

XYScanNet：用于单幅图像去模糊的状态空间模型

arXiv:2412.10338v4 Announce Type: replace 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 visual data by leveraging a flatten-and-s…

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报道来源 [1]

XYScanNet：用于单幅图像去模糊的状态空间模型

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