XYScanNet: A State Space Model for Single Image Deblurring
Journal:
arXiv
Published Date:
Dec 13, 2024
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-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.
