Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling

• URL: http://arxiv.org/abs/2602.10792v1
• Date: Wed, 11 Feb 2026 12:34:37 GMT
• Title: Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling
• Authors: Yi Zhang, Rui Guo, Yonina C. Eldar,
• Abstract summary: In signal processing, the estimation of components of interest is a crucial pre-processing step.<n>We develop a framework for a signal component decomposition, which combines Gibbs sampling with plug-and-play ( plug-and-play ( DiG)<n>We show that DiG can be interpreted as an extension of a class recently proposed diffusion-based samplers, and that, for suitable classes of sensing operators, DiG better exploits the structure of the measurement structure.
• Score: 63.87118366026116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In signal processing, the data collected from sensing devices is often a noisy linear superposition of multiple components, and the estimation of components of interest constitutes a crucial pre-processing step. In this work, we develop a Bayesian framework for signal component decomposition, which combines Gibbs sampling with plug-and-play (PnP) diffusion priors to draw component samples from the posterior distribution. Unlike many existing methods, our framework supports incorporating model-driven and data-driven prior knowledge into the diffusion prior in a unified manner. Moreover, the proposed posterior sampler allows component priors to be learned separately and flexibly combined without retraining. Under suitable assumptions, the proposed DiG sampler provably produces samples from the posterior distribution. We also show that DiG can be interpreted as an extension of a class of recently proposed diffusion-based samplers, and that, for suitable classes of sensing operators, DiG better exploits the structure of the measurement model. Numerical experiments demonstrate the superior performance of our method over existing approaches.

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