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Robust object removal with an exemplar-based image inpainting approach

文献类型：期刊论文

作者Wang, Jing1,4; Lu, Ke1; Pan, Daru1; He, Ning2; Bao, Bing-kun3
刊名NEUROCOMPUTING
出版日期2014-01-10
卷号123页码:150-155
关键词Object removal Image inpainting Exemplar Filling priority Similarity
英文摘要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.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence
研究领域[WOS]Computer Science
收录类别SCI
语种英语
WOS记录号WOS:000326909600016
公开日期2015-09-22
源URL[http://ir.ia.ac.cn/handle/173211/8070]  
专题自动化研究所_模式识别国家重点实验室_多媒体计算与图形学团队
作者单位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
推荐引用方式
GB/T 7714		 Wang, Jing,Lu, Ke,Pan, Daru,et al. Robust object removal with an exemplar-based image inpainting approach[J]. NEUROCOMPUTING,2014,123:150-155.
APA Wang, Jing,Lu, Ke,Pan, Daru,He, Ning,&Bao, Bing-kun.(2014).Robust object removal with an exemplar-based image inpainting approach.NEUROCOMPUTING,123,150-155.
MLA Wang, Jing,et al."Robust object removal with an exemplar-based image inpainting approach".NEUROCOMPUTING 123(2014):150-155.

入库方式： OAI收割

来源：自动化研究所

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