Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision

As a convex relaxation of the rank minimization model, the nuclear norm minimization (NNM) problem has been attracting significant research interest in recent years. The standard NNM regularizes each singular value equally, composing an easily calculated convex norm. However, this restricts its capa...

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Veröffentlicht in:	International journal of computer vision Jg. 121; H. 2; S. 183 - 208
Hauptverfasser:	Gu, Shuhang, Xie, Qi, Meng, Deyu, Zuo, Wangmeng, Feng, Xiangchu, Zhang, Lei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2017 Springer Springer Nature B.V
Schlagworte:	Artificial Intelligence Computer Imaging Computer Science Convexity Image Processing and Computer Vision Low level Mathematical analysis Mathematical models Minimization Noise reduction Norms Optimization Pattern Recognition Pattern Recognition and Graphics Quadratic programming Solvers State of the art Subtraction Vision Weight Low level vision Nuclear norm minimization Low rank analysis
ISSN:	0920-5691, 1573-1405
Online-Zugang:	Volltext
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