改进Mamba模型的计算方式，提升轻量级图像超分辨率性能
First-order State Space Model for Lightweight Image Super-resolution
- 用一阶保持条件重构SSM离散化形式，增强特征关联
- 在5个基准数据集上超越现有轻量级超分方法，参数量不变
- 适合追求高效高精度图像重建的研究者和开发者
状态空间模型（SSMs），特别是Mamba，在自然语言处理中表现出色，并逐渐被应用于视觉任务。然而，大多数基于Mamba的视觉模型关注网络架构和扫描路径，对SSM模块本身关注不足。为探索SSM潜力，本文在不增加参数量的前提下，改进了SSM的计算过程，以提升轻量级超分辨率任务表现。提出一阶状态空间模型（FSSM），通过引入一阶保持条件，推导新的离散化形式并分析累积误差。大量实验表明，FSSM在五个基准数据集上提升了MambaIR的性能，优于当前轻量级超分辨率方法，达到领先水平。
原文摘要 · Abstract (English)
State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with little attention to the SSM module. In order to explore the potential of SSMs, we modified the calculation process of SSM without increasing the number of parameters to improve the performance on lightweight super-resolution tasks. In this paper, we introduce the First-order State Space Model (FSSM) to improve the original Mamba module, enhancing performance by incorporating token correlations. We apply a first-order hold condition in SSMs, derive the new discretized form, and analyzed cumulative error. Extensive experimental results demonstrate that FSSM improves the performance of MambaIR on five benchmark datasets without additionally increasing the number of parameters, and surpasses current lightweight SR methods, achieving state-of-the-art results.
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