New State Space Model Architecture Enhances Image Deblurring

Researchers have introduced XYScanNet, a novel architecture for single image deblurring that utilizes a State Space Model (SSM) approach. Unlike previous Mamba-based methods that convert image patches into 1D sequences, XYScanNet employs a 'slice-and-scan' strategy to better preserve local pixel dependencies and improve sharpness. This new module, called the Vision State Space Module (VSSM), addresses efficiency challenges in current Mamba vision models and has demonstrated competitive distortion metrics and enhanced perceptual performance, outperforming competitors in KID metrics by 17%. AI

IMPACT Introduces a novel approach to image deblurring using State Space Models, potentially improving performance in low-level vision tasks.

RANK_REASON The cluster contains an academic paper detailing a new model architecture for a specific computer vision task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• arXiv
• CNN
• Hanzhou Liu
• Mamba
• State Space Model
• transformer
• Vision State Space Module
• XYScanNet

• paper
• model release
• other

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