Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo

• URL: http://arxiv.org/abs/2502.06379v1
• Date: Mon, 10 Feb 2025 11:59:02 GMT
• Title: Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo
• Authors: Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten,
• Abstract summary: We design a sequential Monte Carlo method for linear-Gaussian inverse problems. We demonstrate the effectiveness of our Decoupled Sequential Monte Carlo (DDSMC) algorithm on both synthetic data and image reconstruction tasks.
• Score: 11.629137473977888
• License:
• Abstract: A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designing a sequential Monte Carlo method for linear-Gaussian inverse problems which builds on ``decoupled diffusion", where the generative process is designed such that larger updates to the sample are possible. The method is asymptotically exact and we demonstrate the effectiveness of our Decoupled Diffusion Sequential Monte Carlo (DDSMC) algorithm on both synthetic data and image reconstruction tasks. Further, we demonstrate how the approach can be extended to discrete data.

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