Diffusion models have emerged as the leading approach for high-quality image synthesis and demonstrate exceptional versatility in solving inverse problems through their powerful learned image priors. In this seminar, we explore how these generative priors enable adaptive compressed sensing for real-world active acquisition applications, including MRI and CT imaging, where intelligent measurement selection can dramatically reduce scan times while preserving reconstruction quality.
We further demonstrate how these same principles extend naturally to image compression, leveraging the diffusion prior to achieve efficient encoding and high-fidelity reconstruction.
Motivated by limitations in existing posterior sampling methods, we introduce a novel model architecture specifically designed for inverse problems that is both theoretically justified and computationally efficient. Collectively, these contributions establish a unified framework for deploying diffusion models across medical imaging, image compression, and image restoration, advancing both the practical applicability and theoretical foundations of generative models for inverse problems.
Noam Elata is a Ph.D. candidate under the supervision of Prof. Michael Elad and Prof. Tomer Michaeli.
