MBMamba: When Memory Buffer Meets Mamba for Structure-Aware Image Deblurring
This is an incremental improvement for image deblurring tasks, enhancing real-time performance without altering the core Mamba architecture.
The paper tackled the problem of image deblurring by addressing local pixel forgetting and channel redundancy in Mamba architectures, resulting in a method that outperforms state-of-the-art approaches on benchmarks.
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.