Provable Diffusion-based Posterior Sampling for Linear Inverse Problems via DDIM
Score
79提出了一种新颖且理论完备的扩散后验采样方法，对现有方法有显著改进。
内容严谨，包含理论证明与充分实验，逻辑清晰。
算法简单高效，易于实现，适用于多种逆问题，实用价值高。
论文发表于arXiv 2607，反映最新进展，时效性强。
arXiv预印本，作者来自知名机构，但尚未经同行评审。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
Combines theoretical proof and empirical validation with a simple and easy-to-implement algorithm, representing significant progress in diffusion-based inverse problem solving.
AI commentary
「This paper reduces diffusion-based posterior sampling to coordinate-wise DDIM updates with a clean theoretical proof, marking a significant step for diffusion-based inverse problem solving. It is particularly valuable for researchers in image restoration and compressed sensing, offering an efficient and provable practical tool.」
Quick overview
This paper proposes a simple and efficient algorithm called PDDIM for solving linear inverse problems with diffusion priors via a DDIM-type sampler. The method requires only lightweight, coordinate-wise modifications to the standard DDIM update while explicitly incorporating the measurement model. The key idea is to perform posterior sampling separately along each singular direction: when the observation SNR is below the diffusion SNR, it follows the learned prior; otherwise, it switches to a calibrated measurement-based predictor. The sampler is proven to converge to the Bayesian posterior conditioned on measurements. Empirical results show favorable performance on image restoration tasks.
- Proposes PDDIM algorithm, requiring only coordinate-wise modifications to DDIM for posterior sampling.
- Provides theoretical proof that the sampler converges to the conditional posterior distribution.
- Achieves state-of-the-art performance on most metrics in image restoration tasks.
Full text
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