题目： A Unified Approach for Data Synthesis in Imaging: Integrating Paired and Unpaired Datasets

内容简介：A significant gap between theory and practice in imaging sciences arises from inaccuracies in mathematical models, including imperfect imaging models and complex noise. Recent advancements have seen deep neural networks directly mapping observed data to clean images using paired training data. While these approaches deliver promising results across various tasks, collecting paired training data remains challenging and resource-intensive in practice. To address this limitation, we propose a unified generative model capable of leveraging both paired and unpaired data during training. Once trained, the model can generate high-quality synthetic data for direct use in downstream tasks. Experimental results on diverse real-world datasets demonstrate the effectiveness of the proposed methods. Finally, I will present recent progress in addressing the preferred orientation problem in cryo-EM, showcasing how these tools contribute to advancing the field.

报告人：包承龙

报告人简介：清华大学丘成桐数学科学中心长聘副教授、北京雁栖湖应用数学研究院副教授、清华大学膜生物学全国重点实验室研究员。研究兴趣主要在人工智能、图像处理和最优化算法方面，已在各类期刊和会议上发表学术论文50余篇。入选国家高层次青年人才项目、获CSIAM应用数学青年科技奖、ORSC青年科技奖。担任SIIMS编委，主持多项科技部、基金委和企事业单位单位项目。

时  间：2025年4月8日（周二）上午10:00开始

地  点：腾讯会议 314-957-068

热烈欢迎广大师生参加!

       信息科学技术学院

2025年4月7日