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.

翻译：扩散模型已成为解决逆问题的强大学习先验。然而，当前交替进行扩散采样与数据一致性步骤的迭代求解方法，由于误差累积，通常需要数百甚至数千步才能实现高质量重建。我们通过SURE引导的后验采样（SGPS）应对这一挑战，该方法利用斯坦无偏风险估计（SURE）梯度更新和基于主成分分析（PCA）的噪声估计来校正采样轨迹偏差。通过在关键的前期和中期采样阶段减轻噪声引起的误差，SGPS能够实现更精确的后验采样并减少误差累积。这使得我们的方法能够在少于100次神经函数评估（NFEs）的情况下保持高重建质量。我们在多种逆问题上进行的广泛评估表明，在低NFE条件下，SGPS始终优于现有方法。