Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling Article Swipe

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Yongqiang Zhang , Rui Guo , Yonina C. Eldar ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2509.12857

We propose a posterior sampling algorithm for the problem of estimating multiple independent source signals from their noisy superposition. The proposed algorithm is a combination of Gibbs sampling method and plug-and-play (PnP) diffusion priors. Unlike most existing diffusion-model-based approaches for signal separation, our method allows source priors to be learned separately and flexibly combined without retraining. Moreover, under the assumption of perfect diffusion model training, the proposed method provably produces samples from the posterior distribution. Experiments on the task of heartbeat extraction from mixtures with synthetic motion artifacts demonstrate the superior performance of our method over existing approaches.

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2509.12857
PDF: https://arxiv.org/pdf/2509.12857
OA Status: green
OpenAlex ID: https://openalex.org/W4415317148

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OpenAlex ID: https://openalex.org/W4415317148

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DOI: https://doi.org/10.48550/arxiv.2509.12857

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Title: Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling

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Type: preprint

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Language: en

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Publication year: 2025

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Publication date: 2025-09-16

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Authors: Yongqiang Zhang, Rui Guo, Yonina C. Eldar

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Landing page: https://arxiv.org/abs/2509.12857

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PDF URL: https://arxiv.org/pdf/2509.12857

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2509.12857

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Cited by: 0

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