5 papers · 1 filter
Learning to Replace MCMC in Split-Gibbs Diffusion Posterior Sampling via Deep Unfolding
Yi Zhang, Rui Guo, Mengchu Xu +2
Split Gibbs sampling enables diffusion posterior inference for general nonlinear inverse problems by decoupling prior and likelihood computations, allowing a pretrained diffusion p…
Physics Informed Deep Unfolded Full Waveform Inversion for Edema Detection
Ruizhi Zhang, Yhonatan Kvich, Rui Guo +2
Edema is a potential indicator of underlying pathological changes. However, its low-contrast signature is often masked in conventional B-mode imaging by strong scatterers, making r…
Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling
Yi Zhang, Rui Guo, Yonina C. Eldar
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…
Bayesian Signal Separation via Plug-and-Play Diffusion-Within-Gibbs Sampling
Yi Zhang, Rui Guo, Yonina C. Eldar
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…
Plug-and-Play Latent Diffusion for Electromagnetic Inverse Scattering with Application to Brain Imaging
Rui Guo, Yi Zhang, Yhonatan Kvich +3
Electromagnetic (EM) imaging is an important tool for non-invasive sensing with low-cost and portable devices. One emerging application is EM stroke imaging, which enables early di…