文件名称：Semi-global weighted least
文件大小：3.68MB
文件格式：PDF
更新时间：2021-01-03 06:33:15
machine learning, cnn, weighted least,iccv
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 ap- proximation 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 pro- pose to iteratively solve a sequence of subsystems which are one-dimensional WLS models. Although each subsys- tem is one-dimensional, it can take two-dimensional neigh- borhood information into account due to the proposed spe- cial 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 meth- ods mainly focus on the 4-connected/8-connected neighbor- hood system, our SG-WLS can handle a more general and larger neighborhood system thanks to the proposed fast so- lution. We show such a generalization can achieve better performance than the 4-connected/8-connected neighbor- hood system in some applications. Our SG-WLS is 20 times faster than the WLS model. For an image of M × N, the memory cost of SG-WLS is at most at the magnitude of max{ 1 M , 1 N } of that of the WLS model. We show the effec- tiveness and efficiency of our SG-WLS in a range of appli- cations.