用循环状态空间模型修复老照片多重退化，效果优于现有方法。
Loop-Mamba: A Loop Mamba with Degradation-Aware and Shared Memory for Old Photo Restoration
- 通过迭代状态演化建模修复过程，保持持久的恢复状态。
- 提出退化估计器精准识别局部损伤与整体退化程度。
- 轻量级设计适合低资源环境，特别适合老照片修复任务。
老照片常受划痕、裂纹、褪色、模糊、噪声和缺损等多种耦合退化影响，严重降低视觉质量与语义内容。本文提出轻量级循环状态空间框架 Loop-Mamba，将老照片修复建模为渐进式状态演化过程，通过持续传播与优化持久恢复状态实现修复。提出语义引导退化估计器（SGDE），联合预测局部退化图与全局退化评分，为状态演化提供退化感知指导。进一步设计共享结构记忆 Mamba（S²M-Mamba），在迭代间维持持久恢复状态，支持鲁棒的长距离结构重建。得益于一阶状态递归，Loop-Mamba 通过循环转移传播潜在恢复状态，而非重复堆叠深层特征变换，有效缓解梯度稀释并避免传统迭代 CNN 与 Transformer 框架的计算开销。引入轻量级多方向扫描策略，增强方向信息聚合与结构连续性保持。为更准确评估修复质量，提出面向任务的老照片损伤恢复评分（ODRS），综合衡量退化恢复与结构重建保真度。在 SynOld 公共基准上的实验表明，Loop-Mamba 在常规指标与 ODRS 上均持续优于现有最先进方法。
原文摘要 · Abstract (English)
Old photographs often suffer from multiple coupled degradations, including scratches, cracks, fading, blur, noise, and missing regions, severely degrading both visual quality and semantic content. We propose Loop-Mamba, a lightweight loop-based state-space framework that formulates old photo restoration as progressive state evolution, where a persis- tent restoration state is continuously propagated and refined through iterative computation. Specifically, we introduce a Semantic-Guided Degradation Estimator (SGDE) to explicitly model heterogeneous degradations by jointly predicting local degradation maps and global degradation scores, providing degradation-aware guidance for state evolution. We further develop a Shared Structural Memory Mamba (S$^2$M- Mamba), which maintains a persistent restoration state across iterations, enabling persistent state evolution through shared structural memory for robust long-range structural reconstruction. Benefiting from first-order state recursion, Loop-Mamba propagates latent restoration states through recurrent tran- sitions instead of repeatedly stacking deep feature transformations, thereby alleviating gradient dilution while avoiding the computational overhead inherent in iterative CNN- and Transformer-based restoration frameworks. A lightweight multi-directional scanning strategy further enhances direc- tional information aggregation and preserves structural continuity. To better evaluate restoration quality, we introduce the task-oriented Old Photo Damage Recovery Score (ODRS), which jointly measures degradation recovery and structural reconstruction fidelity. Experimental results on the public SynOld benchmark demonstrate that Loop-Mamba consistently outperforms previous state-of-the-art methods across both conventional restoration metrics and the proposed ODRS.
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