Open AccessImage inpainting based on low‐rank and joint‐sparse matrix recovery
Author(s) -
Chen DaiQiang,
Cheng LiZhi
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2012.3054
Subject(s) - inpainting , joint (building) , sparse matrix , rank (graph theory) , artificial intelligence , matrix (chemical analysis) , computer vision , low rank approximation , computer science , image (mathematics) , mathematics , pattern recognition (psychology) , algorithm , combinatorics , materials science , engineering , hankel matrix , physics , mathematical analysis , architectural engineering , composite material , quantum mechanics , gaussian
Image inpainting is a classical inverse problem of image science and has many applications. In the previous works, most of the variational inpainting methods can be considered as special cases of the restoration model where the linear operator is just the project to the known indexes. In this reported work, the variational inpainting model is established from the view of image decomposition. Then the unknown component can be recovered by the known component under the low‐rank and joint‐sparse constraints. Numerical experiments demonstrate that the proposed algorithm outperforms most of the current state‐of‐the‐art methods with respect to the peak‐signal‐to‐noise ratio value.