文献类型：期刊文献

英文题名：Robust object removal with an exemplar-based image inpainting approach

作者：Wang, Jing[1,4];Lu, Ke[1];Pan, Daru[1];He, Ning[2];Bao, Bing-kun[3]

第一作者：Wang, Jing

通讯作者：Lu, K[1]

机构：[1]Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China;[2]Beijing Union Univ, Sch Informat, Beijing 100101, Peoples R China;[3]Chinese Acad Sci, Inst Automat, Beijing 100049, Peoples R China;[4]Henan Polytech Univ, Coll Comp Sci & Technol, Jiaozuo 454000, Peoples R China

第一机构：Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China

通讯机构：[1]corresponding author), Chinese Acad Sci, Grad Univ, Coll Comp & Commun Engn, Beijing 100049, Peoples R China.

年份：2014

卷号：123

起止页码：150-155

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20134316891108);Scopus(收录号：2-s2.0-84885837632);WOS:【SCI-EXPANDED(收录号:WOS:000326909600016)】；

基金：This work was supported by the China Special Fund for Meteorological-Scientific Research in the Public Interest (GYHY201106044), NSFC (Grant nos. 61103130, 61070120, and 61141014), National Program on Key Basic Research Project (973 Programs) (Grant nos. 2010CB731804-1 and 2011CB706901-4).

语种：英文

外文关键词：Object removal; Image inpainting; Exemplar; Filling priority; Similarity

摘要：Object removal can be accomplished by an image inpainting process which obtains a visually plausible image interpolation of an occluded or damaged region. There are two key components in an exemplar-based image inpainting approach: computing filling priority of patches in the missing region and searching for the best matching patch. In this paper, we present a robust exemplar-based method. In the improved model, a regularized factor is introduced to adjust the patch priority function. A modified sum of squared differences (SSD) and normalized cross correlation (NCC) are combined to search for the best matching patch. We evaluate the proposed method by applying it to real-life photos and testing the removal of large objects. The results demonstrate the effectiveness of the approach. (C) 2013 Published by Elsevier B.V.