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

内容簡介：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.

報告人：包承龍

報告人簡介：清華大學丘成桐數學科學中心長聘副教授、北京雁栖湖應用數學研究院副教授、清華大學膜生物學全國重點實驗室研究員。研究興趣主要在人工智能、圖像處理和最優化算法方面，已在各類期刊和會議上發表學術論文50餘篇。入選國家高層次青年人才項目、獲CSIAM應用數學青年科技獎、ORSC青年科技獎。擔任SIIMS編委，主持多項科技部、基金委和企事業單位單位項目。

時  間：2025年4月8日（周二）上午10:00開始

地  點：騰訊會議 314-957-068

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2025年4月7日