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Spatially Adapted First and Second Order Regularization for Image Reconstruction: From Image Surface Perspective
作者:    时间:2019-04-08 浏览次数:

  

报告人: 段玉萍

工作单位: 天津大学

报告时间:2019410日上午10:00

报告地点:数学与统计学院一楼南研究生教室

报告摘要:

It is well-known that images are comprised of multiple objects at different scales. Thus, we propose a spatially adapted first and second order regularization for image reconstruction to better localize image features. More specifically, we derive the regularization by pursuing the total variation norm of the unit normal of the associated image surface for a given image, which is proven with good geometric attribute in preserving image contrast. In what follows, we present the numerical solution to the proposed model by employing the alternating direction method of multipliers (ADMM) and provide its analytical properties. Various numerical experiments on image denoising, deblurring and inpainting are implemented to demonstrate the effectiveness and efficiency of the proposed regularization scheme. By comparing with several state-of-the-art methods on synthetic and real image reconstruction problems, the proposal is shown can not only enhance image regions containing fine details and smoothness in homogeneous regions, but also well preserve edges and image contrast.

 

报告人简介:

天津大学应用数学中心, 教授,博士生导师, 第12批国家青年千人计划入选者,主持国家自然科学基金青年基金和天津市重大专项各一项。2007至2011年在新加坡南洋理工大学攻读博士学位。2012年至2015年在新加坡科技研究局资讯通信研究院工作,担任二级研究科学家,参与过多个医学工程项目。主要从事数字图像处理、手术模拟等领域的研究。在IEEE Trans. Image Process.,IEEE Trans. Medical Imaging等期刊和会议发表论文20余篇。