用记忆缓冲提升Mamba对图像结构的感知，实现更优去模糊效果。
MBMamba: When Memory Buffer Meets Mamba for Structure-Aware Image Deblurring
- 引入记忆缓冲保存历史信息，增强相邻像素关联建模
- 设计伊辛启发的正则化损失，保持图像结构一致性
- 不改动原Mamba结构，兼顾性能与实时性，适合工业部署
Mamba架构已成为图像去模糊的有力候选，但其平铺扫描策略常导致局部像素遗忘和通道冗余，难以有效聚合二维空间信息。现有方法通过修改扫描方式或引入局部特征模块缓解问题，却增加计算开销，影响实时性。本文提出无需改变原始Mamba结构的结构感知去模糊网络MBMamba。设计记忆缓冲机制，保留历史信息用于后续融合，实现相邻特征间可靠相关性建模；同时引入伊辛启发的正则化损失，模拟物理系统中像素间的“相互吸引”能量最小化过程，维持图像结构与连贯性。实验表明，该方法在多个主流基准上优于当前最优方案。
原文摘要 · Abstract (English)
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.
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