Computationally Efficient Truncated Nuclear Norm Minimization for High Dynamic Range Imaging
Matrix completion is a rank minimization problem to recover a low-rank data matrix from a small subset of its entries. Since the matrix rank is nonconvex and discrete, many existing approaches approximate the matrix rank as the nuclear norm. However, the truncated nuclear norm is known to be a bette...
| Publié dans: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 25(2016), 9 vom: 05. Sept., Seite 4145-57 |
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| Format: | Article en ligne |
| Langue: | English |
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2016
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| Accès à la collection: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Sujets: | Journal Article |
| Accès en ligne |
Volltext |