报告题目：Extra Proximal-Gradient Inspired Non-local Network
报告人：    陈韵梅 教授  University of Florida, USA
主持人：    沈超敏 副教授
报告时间： 2019年6月5日  周三10:00-11:00
报告地点： 理科大楼B1002

报告摘要：
Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision.  To take advantages of advanced optimization algorithms and powerful representation ability of deep neural networks, we propose a novel deep network for image reconstruction and restoration.  The architecture of this network is inspired by our proposed accelerated extra proximal gradient algorithm. It is able to incorporate non-local operation to  xploit the non-local self-similarity of the images and to learn the  nonlinear transform, under which the solution is sparse. Our experimental results showed that our network outperforms several state-of-the-art deep networks with similar number or only slightly increased number of learnable parameter.

报告人简介：
陈韵梅，现任Florida大学杰出教授。 曾获中华人民共和国国家自然科学三等奖、中华人民共和国教育部科技进步一等奖。陈韵梅教授主要致力于数学和图像科学这一交叉学科的研究。研究课题不仅包括图像分析中数学模型的建立与数值方法的发展，而且对其潜在的数学理论进行了进一步的探索。她在国际最顶尖成像期刊发表多篇具有重要影响的学术论文。陈韵梅教授被公认为偏微分方程与图像处理领域内的世界级科学家，在国际上具有崇高的学术地位。