EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
Journal:
arXiv
Published Date:
Jun 27, 2025
Abstract
Image restoration is a key task in low-level computer vision that aims to
reconstruct high-quality images from degraded inputs. The emergence of Vision
Mamba, which draws inspiration from the advanced state space model Mamba, marks
a significant advancement in this field. Vision Mamba demonstrates excellence
in modeling long-range dependencies with linear complexity, a crucial advantage
for image restoration tasks. Despite its strengths, Vision Mamba encounters
challenges in low-level vision tasks, including computational complexity that
scales with the number of scanning sequences and local pixel forgetting. To
address these limitations, this study introduces Efficient All-Around Mamba
(EAMamba), an enhanced framework that incorporates a Multi-Head Selective Scan
Module (MHSSM) with an all-around scanning mechanism. MHSSM efficiently
aggregates multiple scanning sequences, which avoids increases in computational
complexity and parameter count. The all-around scanning strategy implements
multiple patterns to capture holistic information and resolves the local pixel
forgetting issue. Our experimental evaluations validate these innovations
across several restoration tasks, including super resolution, denoising,
deblurring, and dehazing. The results validate that EAMamba achieves a
significant 31-89% reduction in FLOPs while maintaining favorable performance
compared to existing low-level Vision Mamba methods.
