MambaIRv2让图像修复模型突破因果限制，一次扫描完成全局感知。
MambaIRv2: Attentive State Space Restoration
- 引入非因果状态空间方程，实现跨序列像素注意力
- 轻量级模型参数减少9.3%，超清修复提升0.35dB PSNR
- 适合追求高效高质图像恢复的开发者和研究者
基于Mamba的图像修复骨干网络虽在全局感知与计算效率间取得良好平衡，但其固有的因果建模机制（每个像素仅依赖前序像素）限制了图像全域信息利用，带来新挑战。本文提出MambaIRv2，通过引入类ViT的非因果建模能力，实现关注远距离像素的注意力机制。所提的注意力状态空间方程支持在单次扫描中完成图像展开，同时设计语义引导的邻域交互机制，促进远距离相似像素间的协作。大量实验表明，MambaIRv2在轻量级超分辨率任务中比SRFormer高出0.35dB PSNR，参数减少9.3%；在经典超分辨率任务上相较HAT最高提升0.29dB。代码已公开于https://github.com/csguoh/MambaIR。
原文摘要 · Abstract (English)
The Mamba-based image restoration backbones have recently demonstrated significant potential in balancing global reception and computational efficiency. However, the inherent causal modeling limitation of Mamba, where each token depends solely on its predecessors in the scanned sequence, restricts the full utilization of pixels across the image and thus presents new challenges in image restoration. In this work, we propose MambaIRv2, which equips Mamba with the non-causal modeling ability similar to ViTs to reach the attentive state space restoration model. Specifically, the proposed attentive state-space equation allows to attend beyond the scanned sequence and facilitate image unfolding with just one single scan. Moreover, we further introduce a semantic-guided neighboring mechanism to encourage interaction between distant but similar pixels. Extensive experiments show our MambaIRv2 outperforms SRFormer by even 0.35dB PSNR for lightweight SR even with 9.3\% less parameters and suppresses HAT on classic SR by up to 0.29dB. Code is available at https://github.com/csguoh/MambaIR.
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