Research Paper#Diffusion Models, Inverse Problems, Machine Learning🔬 ResearchAnalyzed: Jan 3, 2026 19:04
SURE Guided Posterior Sampling for Faster Inverse Problem Solving
Analysis
This paper introduces a novel method, SURE Guided Posterior Sampling (SGPS), to improve the efficiency of diffusion models for solving inverse problems. The core innovation lies in correcting sampling trajectory deviations using Stein's Unbiased Risk Estimate (SURE) and PCA-based noise estimation. This approach allows for high-quality reconstructions with significantly fewer neural function evaluations (NFEs) compared to existing methods, making it a valuable contribution to the field.
Key Takeaways
- •Proposes SURE Guided Posterior Sampling (SGPS) to improve the efficiency of diffusion models for inverse problems.
- •Uses SURE and PCA-based noise estimation to correct sampling trajectory deviations.
- •Achieves high-quality reconstructions with significantly fewer neural function evaluations (NFEs).
- •Outperforms existing methods at low NFE counts across diverse inverse problems.
Reference
“SGPS enables more accurate posterior sampling and reduces error accumulation, maintaining high reconstruction quality with fewer than 100 Neural Function Evaluations (NFEs).”