讲座题目🛰🚵：Image smoothing via gradient sparsity and surface area minimization 主讲人：曾铁勇 副教授 开始时间：2019-11-30 14:35:00 讲座地址：中北校区办公楼小礼堂 主办单位🙍🏻♂️🐦：计算机科学与技术学院
报告人简介🎟： 曾铁勇👨🏿🎓，香港中文大学数学系，2000年本科毕业于北京大学🦢，2004年法国综合理工大学获硕士学位，2007年巴黎第十三大学获得博士学位🦤。2007-2008年在法国数学和应用研究中心从事博士后研究；2008入职香港浸会大学，先后任助理教授👩🏻🔬、副教授；2018年入职香港中文大学，任副教授。主要研究领域包括优化理论，图像处理🍙，反问题🥇，统计/机器学习，科学计算等📪。在优化🪻、图像处理↗️、反问题的国际一流杂志SIAM Journal on Imaging Sciences, SIAM Journal on Scientific Computing, International Journal of Computer Vision, Journal of Scientific Computing🤜🏿，IEEE Transactions on Image Processing，Pattern Recognition，Journal of Mathematical Imaging and Vision🤫📢，Inverse Problems & Imaging等发表过80余篇SCI论文👨🏼🦲，谷歌学术引用2400余次。 报告内容☎️： Image smoothing is a very important topic in image processing. Among these image smoothing methods, the L0 gradient minimization method is one of the most popular ones. However, the L0 gradient minimization method suffers from the staircasing effect and over-sharpening issue, which highly degrade the quality of the smoothed image. To overcome these issues, we use not only the L0 gradient term for finding edges, but also a surface area based term for the purpose of smoothing the inside of each region. An alternating minimization algorithm is suggested to efficiently solve the proposed model, where each subproblem has a closed-form solution. Leveraging the introduced surface area term, the proposed method can effectively alleviate the staircasing effect and the over-sharpening issue. The superiority of our method over the state-of-the-art methods is demonstrated by a series of experiments. |
