%0 Journal Article
%T Robust gradient driving image inpainting method<br>鲁棒的梯度驱动图像修复算法
%A Ye Xueyi
%A Wang Jing
%A Zhao Zhijing
%A Chen Huahua
%A <br>叶学义
%A 王靖
%A 赵知劲
%A 陈华华
%J 中国图象图形学报
%D 2012
%I 
%X Gradient-driven PDEs (partial differential equations) are the main computing pattern for geometric inpainting models of digital images.Apparently,compared with previous models,gradient-driven computing models have a great advantage to the large-scale regions geometric inpainting,but its performances are not stable to different inpainted objects because the information propagating direction is uncertain in the inpainting process.Based on analyzing the computing essences and the corresponding physical meanings of gradient-driven models,it is decisive to the inpainting result that the information propagating direction always points to the outside of the inpainted regions.Thus,a new method of gradient-driven image inpainting is proposed.Experimental results prove that the method can stabilize the information propogating direction making its inpainting performance is more robust.
%K image restoration
%K partial differential equations
%K gradient driving
%K information propagating direction<br>数字图像修复
%K 偏微分方程
%K 梯度驱动
%K 信息传播方向
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=C4354F1915DDC64013B1391D6FA093C8&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=B31275AF3241DB2D&sid=039DCCB9394D9766&eid=20C9FB8C7B4A22AD&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=15