Comments on "A closed-form nonparametric Bayesian estimator in the wavelet domain of images using an approximate α-stable prior"

  • Authors:

  • Alin Achim;Ercan Kuruoglu;Anastasios Bezerianos;Panagiotis Tsakalides

  • Affiliations:

  • Centre for Communications Research, Department of Electrical and Electronic Engineering, University of Bristol, Merchant Venturers Building, BS8 1UB Bristol, United Kingdom;Signals and Images Laboratory, Istituto di Scienza e Tecnologie dell'Informazione "A. Faedo", Area della Ricerca, CNR di Pisa, Via G. Moruzzi 1, 56124 Pisa, Italy;Biosignal Processing Group, Department of Medical Physics, University of Patras, 265 00 Rio, Greece;Institute of Computer Science, Foundation for Research and Technology-Hellas (ICS-FORTH), Vassilika Vouton, P.O. Box 1385, GR-711 10 Heraklion, Greece

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2007

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Abstract

The purpose of this commentary is to point out that the paper by Boubchir and Fadili (B-F) [Boubchir, L., Fadili, J.M., 2006. A closed-form nonparametric Bayesian estimator in the wavelet domain of images using an approximate @a-stable prior. Pattern Recognit. Lett. 27, 1370-1382] is little more than a paraphrase of earlier work in [Achim, A., Bezerianos, A., Tsakalides, P., 2001. Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imag. 20 (August), 772-783; Kuruoglu, E.E., Molina, C., Fitzgerald, W.J., 1998. Approximation of @a-stable probability densities using finite Gaussian mixtures. In: Proc. EUSIPCO'98 (September); Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P., 2003. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE. Trans. Image. Proc. 12 (November), 1338-1351]. Essentially, B-F purport to propose an algorithm for image denoising based on scale mixtures of Gaussians in the wavelet domain, an approach well documented in [Portilla, J., Strela, V., Wainwright, M.J., Simoncelli, E.P., 2003. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE. Trans. Image. Proc. 12 (November), 1338-1351]. In achieving this, B-F make use of a known method for approximating @a-stable distributions previously proposed by Kuruoglu and co-workers [Kuruoglu, E.E., 1998. Signal processing in @a-stable noise environments: A least lp-norm approach. Ph.D. thesis, University of Cambridge, Cambridge; Kuruoglu, E.E., Molina, C., Fitzgerald, W.J., 1998. Approximation of @a-stable probability densities using finite Gaussian mixtures. In: Proc. USIPCO'98 (September)], but without referring to their work. Together, the above observations do not entitle B-F to claim to have developed a new algorithm. In addition, we show that B-F [2006; Fadili, J.M., Boubchir, L., 2005. Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities. IEEE Trans. Image Process. 14 (2), 231-240] include unfair comments and comparison vis-a-vis of the method proposed in our early work [Achim, A., Bezerianos, A., Tsakalides, P., 2001. Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imag. (August) 20, 772-783].