报告时间：2024年12月26日（星期四）16:30-17:30

报告地点：理学楼609报告厅

报告题目：Physical perception deep adversarial network for restoring atmospheric turbulence degraded images

报告摘要: In this talk, we propose a physical perception Deep Adversarial Network to recover turbulence-degraded images, gradually eliminating noise, geometric distortion, and blurring. The network accurately extracts distortion features by sensing the geometric distortion field in the degraded image and imposes stronger constraints during the correction process for better results. The feature extraction process utilizes dense residual self-attention blocks to effectively capture geometric distortions and other degradation effects, focusing on the correlation between local and overall features. By learning micro-local and macro-global information, the network enhances its ability to perceive distortions and other degradation effects, ensuring coherence between local and overall restoration.

报告人简介：长春理工大学数学与统计学院副教授，硕士研究生导师。主要研究兴趣是数学物理反问题、机器学习算法的设计与理论分析和微分方程数值解等。在JCP，JCAM，CICP，IPI等期刊发表论文20余篇，主持并参与国家自然科学基金、吉林省科技厅基金和吉林省教育厅基金6项。目前担任中国仿真学会不确定系统分析与仿真专委会委员和Math Review评论员。