NDLI: Sparse modeling based image inpainting with local similarity constraint

Content Provider	IEEE Xplore Digital Library
Author	Jingang Shi Chun Qi
Copyright Year	2013
Description	Author affiliation: Sch. of Electron. & Inf. Eng., Xi'an Jiaotong Univ., Xian, China (Jingang Shi; Chun Qi)
Abstract	In this paper, we propose an efficient exemplar-based inpainting algorithm via sparse modeling and local similarity constraint. The inpainting procedure contains two steps: calculating the filling order and reconstructing the target patch. The filling order is decided by patch priority, which privileges the patch located at edge or corner. The target patch is then estimated by a combination of candidate patches. In the proposed method, three regularization terms are introduced to improve the patch reconstruction step. The first term ensures the compatibility between the target patch and the estimated one. The second term assigns larger combination coefficients for the candidate patches which are most similar with the target patch. The third term penalties the combination coefficients for the outliers in the candidate patches. Finally, the three regularization terms are incorporated into a unified sparse representation framework for reconstructing the target patch. Experiments show that the proposed algorithm can effectively fill in missing pixels in a visually plausible way.
Starting Page	1371
Ending Page	1375
File Size	520879
Page Count	5
File Format	PDF
ISBN	9781479923410
DOI	10.1109/ICIP.2013.6738282
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-09-15
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sparse representation Image inpainting Object removal Local similarity constraint
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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