Fast Augmented Lagrangian Method for Image Smoothing with Hyper-Laplacian Gradient Prior

摘要：

　　As a fundamental tool,L0 gradient smoothing has found a flurry of applications.Inspired by the progress of research on hyper-Laplacian prior,we propose a novel model,corresponding to Lp-norm of gradients,for image smoothing,which can better maintain the general structure,whereas diminishing insignificant texture and impulse noise-like highlights.Algorithmically,we use augmented Lagrangian method (ALM) to efficiently solve the optimization problem.Thanks to the fast convergence rate of ALM,the speed of the proposed method is much faster than the L0 gradient method.We apply the proposed method to natural image smoothing,cartoon artifacts removal,and tongue image segmentation,and the experimental results validate the performance of the proposed algorithm.

关键词： Image smoothing augmented Lagrangian method hyper-Laplacian gradient prior

作者: Li Chen Hongzhi Zhang Dongwei Ren David Zhang Wangmeng Zuo

作者单位: Computational Perception and Cognition Center,School of Computer Science and Technology,Harbin Insti Computational Perception and Cognition Center,School of Computer Science and Technology,Harbin Insti

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 12-21

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）