用扩散模型提升视频去雾效果，特别适合医学影像修复。
Nuclear Diffusion Models for Low-Rank Background Suppression in Videos
- 结合低秩建模与扩散采样，重建视频动态内容
- 在心脏超声去雾中提升对比度与信号保真度
- 适合需要高保真视频恢复的医疗或工业场景
视频序列常含有结构化噪声和背景伪影，干扰动态内容的准确分析与还原。传统鲁棒主成分分析（RPCA）通过将数据分解为低秩与稀疏分量来应对，但其稀疏性假设难以捕捉真实视频中的丰富变化。为此，本文提出一种融合低秩时序建模与扩散后验采样的混合框架——核扩散（Nuclear Diffusion）。该方法在真实世界医学成像任务——心脏超声去雾上进行了评估，结果表明其在对比度增强（gCNR）和信号保真度（KS统计量）方面优于传统RPCA。这表明将模型驱动的时序建模与深度生成先验结合，可实现高质量视频恢复。
原文摘要 · Abstract (English)
Video sequences often contain structured noise and background artifacts that obscure dynamic content, posing challenges for accurate analysis and restoration. Robust principal component methods address this by decomposing data into low-rank and sparse components. Still, the sparsity assumption often fails to capture the rich variability present in real video data. To overcome this limitation, a hybrid framework that integrates low-rank temporal modeling with diffusion posterior sampling is proposed. The proposed method, Nuclear Diffusion, is evaluated on a real-world medical imaging problem, namely cardiac ultrasound dehazing, and demonstrates improved dehazing performance compared to traditional RPCA concerning contrast enhancement (gCNR) and signal preservation (KS statistic). These results highlight the potential of combining model-based temporal models with deep generative priors for high-fidelity video restoration.
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