用SURE梯度修正扩散模型采样轨迹，100次内实现高质量图像重建。
SURE Guided Posterior Sampling: Trajectory Correction for Diffusion-Based Inverse Problems
- 基于SURE梯度与主成分噪声估计，动态修正采样偏差。
- 仅需少于100次神经函数评估，即达高保真重建。
- 适合低计算开销、高精度要求的逆问题求解场景。
扩散模型已成为解决逆问题的强大学习先验。然而，现有交替进行扩散采样与数据一致性步骤的迭代方法，常因误差累积需数百甚至上千步才能获得高质量重建。本文提出SURE引导后验采样（SGPS），利用斯坦无偏风险估计（SURE）梯度更新和基于主成分分析（PCA）的噪声估计，修正采样轨迹在早期与中期阶段的偏差。该方法有效缓解了噪声引发的误差，提升后验采样精度并减少误差积累。实验表明，SGPS可在少于100次神经函数评估（NFE）下保持高重建质量，并在多种逆问题上持续优于现有方法。
原文摘要 · Abstract (English)
Diffusion models have emerged as powerful learned priors for solving inverse problems. However, current iterative solving approaches which alternate between diffusion sampling and data consistency steps typically require hundreds or thousands of steps to achieve high quality reconstruction due to accumulated errors. We address this challenge with SURE Guided Posterior Sampling (SGPS), a method that corrects sampling trajectory deviations using Stein's Unbiased Risk Estimate (SURE) gradient updates and PCA based noise estimation. By mitigating noise induced errors during the critical early and middle sampling stages, SGPS enables more accurate posterior sampling and reduces error accumulation. This allows our method to maintain high reconstruction quality with fewer than 100 Neural Function Evaluations (NFEs). Our extensive evaluation across diverse inverse problems demonstrates that SGPS consistently outperforms existing methods at low NFE counts.
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