Double nuclear norm-based matrix decomposition for occluded image recovery and background modeling
Robust principal component analysis (RPCA) is a new emerging method for exact recovery of corrupted low-rank matrices. It assumes that the real data matrix has low rank and the error matrix is sparse. This paper presents a method called double nuclear norm-based matrix decomposition (DNMD) for deali...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 6 vom: 10. Juni, Seite 1956-66 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2015
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't |
| Accès en ligne |
Volltext |