Fractional partial differential equation denoising models for texture image

作     者：PU YiFei SIARRY Patrick ZHOU JiLiu LIU YiGuang ZHANG Ni HUANG Guo LIU YiZhi 

作者机构：School of Computer Science and TechnologySichuan University Universit de Paris 12 (LiSSiE.A.3956) 61 av.du Gnral de Gaulle94010 CRETEIL CedexFrance Library of Sichuan University Sichuan University Computer Science CollegeLeshan Normal University Wu Yuzhang Honors College of Sichuan University 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2014年第57卷第7期

页      面：184-202页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by Foundation Franco-Chinoise Pour La Science Et Ses Applications (FFCSA) National Natural Science Foundation of China (Grant Nos.60972131,61201438) Returned Overseas Chinese Scholars Project of Education Ministry of China (Grant No.20111139) Science and Technology Support Project of Sichuan Province of China (Grant Nos.2011GZ0201,2013SZ0071) Soft Science Project of Sichuan Province of China (Grant No.2013ZR0010) Chengdu Administration of Science and Technology for Transfer of Scientific and Technological Achievements (Grant No.12DXYB255JH-002) 

主　　题：fractional Green formula fractional Euler-Lagrange equation fractional steepest descent approach fractional extreme points fractional total variation fractional differential mask. 

摘      要：In this paper,a set of fractional partial differential equations based on fractional total variation and fractional steepest descent approach are proposed to address the problem of traditional drawbacks of PM and ROF multi-scale denoising for texture *** extending Green,Gauss,Stokes and Euler-Lagrange formulas to fractional field,we can find that the integer formulas are just their special case of fractional *** order to improve the denoising capability,we proposed 4 fractional partial differential equation based multiscale denoising models,and then discussed their stabilities and convergence *** deduction and experimental evaluation demonstrate the stability and astringency of fractional steepest descent approach,and fractional nonlinearly multi-scale denoising capability and best value of parameters are discussed *** experiments results prove that the ability for preserving high-frequency edge and complex texture information of the proposed denoising models are obviously superior to traditional integral based algorithms,especially for texture detail rich images.