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Image denoising using mixtures of projected Gaussian scale mixtures

Bart Goossens UGent, Aleksandra Pizurica UGent and Wilfried Philips UGent (2009) IEEE TRANSACTIONS ON IMAGE PROCESSING. 18(8). p.1689-1702
abstract
We propose a new statistical model for image restoration in which neighbourhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbourhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
MODELS, SIGNAL, NATURAL IMAGES, image denoising, Gaussian scale mixtures, SPARSE, STATISTICS, DOMAIN, Bayesian estimation
journal title
IEEE TRANSACTIONS ON IMAGE PROCESSING
IEEE Trans. Image Process.
volume
18
issue
8
pages
1689 - 1702
Web of Science type
Article
Web of Science id
000268033300001
JCR category
ENGINEERING, ELECTRICAL & ELECTRONIC
JCR impact factor
2.848 (2009)
JCR rank
18/244 (2009)
JCR quartile
1 (2009)
ISSN
1057-7149
DOI
10.1109/TIP.2009.2022006
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1002612
handle
http://hdl.handle.net/1854/LU-1002612
date created
2010-07-05 18:15:35
date last changed
2016-12-19 15:42:12
@article{1002612,
  abstract     = {We propose a new statistical model for image restoration in which neighbourhoods of wavelet subbands are modeled by a discrete mixture of linear projected Gaussian Scale Mixtures (MPGSM). In each projection, a lower dimensional approximation of the local neighbourhood is obtained, thereby modeling the strongest correlations in that neighbourhood. The model is a generalization of the recently developed Mixture of GSM (MGSM) model, that offers a significant improvement both in PSNR and visually compared to the current state-of-the-art wavelet techniques. However the computation cost is very high which hampers its use for practical purposes. We present a fast EM algorithm that takes advantage of the projection bases to speed up the algorithm. The results show that, when projecting on a fixed data-independent basis, even computational advantages with a limited loss of PSNR can be obtained with respect to the BLS-GSM denoising method, while data-dependent bases of Principle Components offer a higher denoising performance, both visually and in PSNR compared to the current wavelet-based state-of-the-art denoising methods.},
  author       = {Goossens, Bart and Pizurica, Aleksandra and Philips, Wilfried},
  issn         = {1057-7149},
  journal      = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
  keyword      = {MODELS,SIGNAL,NATURAL IMAGES,image denoising,Gaussian scale mixtures,SPARSE,STATISTICS,DOMAIN,Bayesian estimation},
  language     = {eng},
  number       = {8},
  pages        = {1689--1702},
  title        = {Image denoising using mixtures of projected Gaussian scale mixtures},
  url          = {http://dx.doi.org/10.1109/TIP.2009.2022006},
  volume       = {18},
  year         = {2009},
}

Chicago
Goossens, Bart, Aleksandra Pizurica, and Wilfried Philips. 2009. “Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures.” Ieee Transactions on Image Processing 18 (8): 1689–1702.
APA
Goossens, B., Pizurica, A., & Philips, W. (2009). Image denoising using mixtures of projected Gaussian scale mixtures. IEEE TRANSACTIONS ON IMAGE PROCESSING, 18(8), 1689–1702.
Vancouver
1.
Goossens B, Pizurica A, Philips W. Image denoising using mixtures of projected Gaussian scale mixtures. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2009;18(8):1689–702.
MLA
Goossens, Bart, Aleksandra Pizurica, and Wilfried Philips. “Image Denoising Using Mixtures of Projected Gaussian Scale Mixtures.” IEEE TRANSACTIONS ON IMAGE PROCESSING 18.8 (2009): 1689–1702. Print.