Paper Info
Title
Image inpainting via sparse representation
Abstract
This paper proposes a novel patch-wise image inpainting algorithm using the image signal sparse representation over a redundant dictionary, which merits in both capabilities to deal with large holes and to preserve image details while taking less risk. Different from all existing works, we consider the problem of image inpainting from the view point of sequential incomplete signal recovery under the assumption that the every image patch admits a sparse representation over a redundant dictionary. To ensure the visually plausibility and consistency constraints between the filled hole and the surroundings, we propose to construct a redundant signal dictionary by directly sampling from the intact source region of current image. Then we sequentially compute the sparse representation for each incomplete patch at the boundary of the hole and recover it until the whole hole is filled. Experimental results show that this approach can efficiently fill in the hole with visually plausible information, and take less risk to introduce unwanted objects or artifacts.
Year
DOI
Venue
2009
10.1109/ICASSP.2009.4959679
ICASSP
Keywords
Field
DocType
novel patch-wise image,redundant dictionary,image detail,redundant signal dictionary,whole hole,image patch,large hole,image signal sparse representation,current image,sparse representation,algorithm design and analysis,information science,pixel,image restoration,noise,voting,dictionaries,dynamic programming,data mining,risk difference,tensile stress,lasso,texture synthesis,belief propagation,indexes
Computer vision,Algorithm design,Pattern recognition,K-SVD,Computer science,Lasso (statistics),Sparse approximation,Inpainting,Artificial intelligence,Pixel,Image restoration,Texture synthesis
Conference
ISSN
Citations 
PageRank 
1520-6149
53
1.61
References 
Authors
8
4
Name
Order
Citations
PageRank
Bin Shen143134.86
Wei Hu218214.17
Yimin Zhang31536130.17
Yu Jin Zhang4127293.14