Speaker: 包承龙副教授，清华大学

Inviter: 刘晓东研究员

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

Language: Chinese

Time & Venue: 2025.12.15 14:00-15:00 N620

Abstract: 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.