报告题目：Recent Advances in Solving Imaging Inverse Problems using Diffusion Models

报告时间：10月20日 10:00

报告地点：五教5206

摘要：

Imaging inverse problems involve reconstructing underlying images from noisy observations. Traditional approaches often rely on handcrafted priors, which can fail to capture the complexity of real-world data. The advent of pre-trained generative models has introduced new paradigms, offering improved reconstructions by learning rich priors from data. Among these, diffusion models have emerged as a powerful framework, achieving remarkable reconstruction performance across numerous imaging inverse problems. In this talk, I will provide an overview of the latest advancements in leveraging diffusion models to address imaging inverse problems, highlighting their technical innovations and practical applications.

个人简介：

Zhaoqiang Liu serves as a professor at both the School of Computer Science and Engineering and the School of Mathematical Sciences, University of Electronic Science and Technology of China (UESTC). His current research focuses on diffusion models and their applications in solving inverse problems, along with theoretical aspects of large language models. He earned his Ph.D. in Mathematics from NUS in 2017 and obtained a Bachelor's degree in Mathematical Sciences from Tsinghua University in 2013.