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Automatic rank estimation of Parafac decomposition and application to multispectral image wavelet denoising

: Zidi, A.; Marot, J.; Bourennane, S.; Spinnler, K.


Karam, L. ; Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
IEEE International Conference on Image Processing, ICIP 2016. Proceedings : September 25-28, 2016, Phoenix Convention Center, Phoenix, Arizona, USA
Piscataway, NJ: IEEE, 2016
ISBN: 978-1-4673-9962-3 (Print)
ISBN: 978-1-4673-9961-6 (Online)
International Conference on Image Processing (ICIP) <23, 2016, Phoenix/Ariz.>
Conference Paper
Fraunhofer IIS ()

There are two main contributions in this paper. Firstly, we estimate the rank for the truncation of the Parafac decomposition in an optimal sense. For this, we propose a least squares criterion and justify the choice of the fast Nelder-Mead method to minimize this criterion. Secondly, we combine the truncation of the Parafac decomposition with multidimensional wavelet packet transform. A single rank value is estimated for each decomposition level, which simplifies the implementation. We exemplify the proposed method with an application to multispectral image denoising: we study the performance of the proposed method based on Parafac decomposition, compared to ForWaRD.