A Fast Augmented Lagrangian Method for Euler’s Elastica Models

作     者：Yuping Duan Yu Wang Jooyoung Hahn 

作者机构：Institute for Infocomm ResearchSingapore Computer Science DepartmentTechnionHaifa 32000Israel Institute for Mathematics and Scientific ComputingUniversity of GrazAustria 

出 版 物：《Numerical Mathematics(Theory,Methods and Applications)》 (高等学校计算数学学报（英文版）)

年 卷 期：2013年第6卷第1期

页      面：47-71页

核心收录：

学科分类：0820[工学-石油与天然气工程] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

主　　题：Euler’s elastica augmented Lagrangian method image denoising image inpainting image zooming 

摘      要：In this paper,a fast algorithm for Euler’s elastica functional is proposed,in which the Euler’s elastica functional is reformulated as a constrained minimization *** the augmented Lagrangian method and operator splitting techniques,the resulting saddle-point problem is solved by a serial of *** tackle the nonlinear constraints arising in the model,a novel fixed-point-based approach is proposed so that all the subproblems either is a linear problem or has a closed-form *** show the good performance of our approach in terms of speed and reliability using numerous numerical examples on synthetic,real-world and medical images for image denoising,image inpainting and image zooming problems.