Diffusion models have recently emerged as powerful generative priors for solving inverse problems. However, training diffusion models is data-intensive and computationally demanding, which restricts their applicability for high-dimensional and high-resolution data such as medical and scientific imaging. In this talk, I will introduce our recent works on how to improve the efficiency (data, time, and memory efficiency) of diffusion-based generative models for solving general inverse problems through posterior sampling. Particularly, I will introduce two plausible solutions we propose to enable learning diffusion priors for solving high-dimensional inverse problems through latent diffusion and patch diffusion models. The results are demonstrated in solving various inverse problems for both natural and medical images including 3D medical image reconstruction, showing the effectiveness of our proposed methods in both model efficiency and model performance. These research open the door to leverage diffusion-based generative models in tackling complex real-world data for addressing various crucial problems in many scientific disciplines.

This presentation will be held in 2036 Palmer Commons. There will also be a remote viewing option via Zoom.