EAMamba：用于图像恢复的高效全方位视觉状态空间模型
EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
AI总结：
本研究提出EAMamba框架，通过集成多头选择性扫描模块（MHSSM）和全方位扫描机制，解决了Vision Mamba在图像恢复任务中的计算复杂度和局部像素遗忘问题，在多项恢复任务中显著降低FLOPs的同时保持良好性能。
摘要：
图像恢复是低级计算机视觉中的一项关键任务，旨在从退化输入中重建高质量图像。Vision Mamba的出现，从先进的状态空间模型Mamba中汲取灵感，标志着该领域的重大进步。Vision Mamba在以线性复杂度对长程依赖进行建模方面表现出色，这是图像恢复任务的关键优势。尽管有其优势，Vision Mamba在低级视觉任务中仍面临挑战，包括随扫描序列数量增加而增加的计算复杂度以及局部像素遗忘问题。为了解决这些限制，本研究引入了Efficient All-Around Mamba (EAMamba)，这是一个增强型框架，集成了具有全方位扫描机制的多头选择性扫描模块（MHSSM）。MHSSM高效聚合多个扫描序列，避免了计算复杂度和参数量的增加。全方位扫描策略实现多种模式以捕获整体信息，并解决了局部像素遗忘问题。我们的实验评估在多个恢复任务中验证了这些创新，包括超分辨率、去噪、去模糊和去雾。结果验证了EAMamba在FLOPs上实现了显著的31-89%减少，同时与现有低级Vision Mamba方法相比保持了良好的性能。
英文摘要：
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.
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