MBMamba: When Memory Buffer Meets Mamba for Structure-Aware Image Deblurring

TL;DR

MBMamba enhances the Mamba architecture for image deblurring by introducing a memory buffer and Ising-inspired regularization, effectively preserving image structure and outperforming existing methods without increasing computational complexity.

Contribution

It proposes a structure-aware deblurring network that maintains the original Mamba architecture while adding a memory buffer and a novel regularization loss for better spatial information modeling.

Findings

Outperforms state-of-the-art on benchmark datasets.

Effectively preserves image structure during deblurring.

Maintains real-time performance without added complexity.

Abstract

The Mamba architecture has emerged as a promising alternative to CNNs and Transformers for image deblurring. However, its flatten-and-scan strategy often results in local pixel forgetting and channel redundancy, limiting its ability to effectively aggregate 2D spatial information. Although existing methods mitigate this by modifying the scan strategy or incorporating local feature modules, it increase computational complexity and hinder real-time performance. In this paper, we propose a structure-aware image deblurring network without changing the original Mamba architecture. Specifically, we design a memory buffer mechanism to preserve historical information for later fusion, enabling reliable modeling of relevance between adjacent features. Additionally, we introduce an Ising-inspired regularization loss that simulates the energy minimization of the physical system's "mutual attraction" between pixels, helping to maintain image structure and coherence. Building on this, we develop MBMamba. Experimental results show that our method outperforms state-of-the-art approaches on widely used benchmarks.