| Abstract: |
| The design of image prior representations is pivotal to solving image inverse problems. With the recent rapid development of deep learning, image priors have evolved from traditional expert-designed and data-driven representations to those learned via generative models. More precise image priors undoubtedly lead to enhanced performance in computational imaging tasks. This talk first provides a brief introduction to the paradigm of learning data distributions using Ordinary Differential Equation (ODE)-controlled generative models. Subsequently, we discuss two perspectives on applying generative priors to image inverse problems: conditional sampling and plug-and-play fusion methods. Finally, the talk outlines the core concepts and computational frameworks of generative algorithms for image inverse problems. |
|