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图像滤波中的半全局加权最小二乘

供稿:    责任编辑:安果    时间:2018-05-18    阅读:次

88125b大爆奖手机版 www.plumbing101.net 主讲人:刘伟

报告时间:20185219:00

报告地点:基础教学楼A303

个人主页:http://www.escience.cn/people/weiliusjtu/index.html

报告题目:Semi-global Weighted Least Squares in Image Filtering

(图像滤波中的半全局加权最小二乘)


报告摘要:

图像滤波中求解全局加权最小二乘(Weighted Least Squares, WLS)需要消耗大量的时间和内存。在本文中,我们提出了一个内存消耗和时间消耗都很小的替代算法,我们把它称为半全局加权最小二乘(Semi-Global Weighted Least Squares, SG-WLS)。与加权最小而中求解一个大型的线性系统不同的是,我们提出迭代求解一系列一维加权最小二乘子系统。虽然每个子系统是一维的,但是由于我们提出了特殊的近邻系统构造方法,这使得每个子系统能够包含二维的近邻信息。这个性质使得我们的半全局加权最小二乘能够和原始的二维加权最小二乘有着相近的表现,但是我们的方法内存消耗和时间消耗更小。以往相关的算法只能够处理4连接或者8连接近邻系统,但是由于我们提出的快速求解算法,使得我们的半全局加权最小二乘能够处理更泛化和更大的近邻系统。这种泛化使得我们的算法能够在一些应用中比4连接或8连接近邻取得更好的结果。我们的半全局加权最小二乘比原始的加权最小二乘快~20倍。

Abstract

Solving the global method of Weighted Least Squares (WLS) model in image filtering is both time- and memory-consuming. In this paper, we present an alternative approximation in a time- and memory- efficient manner which is denoted as Semi-Global Weighed Least Squares (SG-WLS). Instead of solving a large linear system, we propose to iteratively solve a sequence of subsystems which are one-dimensional WLS models. Although each subsystem is one-dimensional, it can take two-dimensional neighborhood information into account due to the proposed special neighborhood construction. We show such a desirable property makes our SG-WLS achieve close performance to the original two-dimensional WLS model but with much less time and memory cost. While previous related methods mainly focus on the 4-connected/8-connected neighborhood system, our SG-WLS can handle a more general and larger neighborhood system thanks to the proposed fast solution. We show such a generalization can achieve better performance than the 4-connected/8-connected neighborhood system in some applications. Our SG-WLS is ~20 times faster than the WLS model. 


报告人简介:

刘伟博士现为阿德莱德大学高级研究员。他博士就读于上海交通大学大学。其主要研究兴趣为图像滤波及其在计算机视觉和图形图像学领域的应用。他在计算机视觉和机器学习领域国际会议(如ICCV,IJCAI)以及期刊(如TIP,TCSVT,TMM)上共发表10余篇论文。他于2017年被授予上海交通大学“学术之星”提名奖(上海交通大学研究生最高学术荣誉称号)。

Dr Wei Liu is a senior research fellow in the University of Adelaide. He had his doctoral research in Shanghai Jiao Tong University. His main research interests mainly focus on image filtering and its applications in computer vision and computational graphics. He has published more than 10 papers in the international conference/journals of computer vision and machine learning, including ICCV, IJCAI, TIP, TCSVT, TMM. He has been awarded the nomination of “Academic Star” of Shanghai Jiao Tong University which is the highest academic honor for graduate students in Shanghai Jiao Tong University.