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Removal of correlated noise by modeling the signal of interest in the wavelet domain

Bart Goossens UGent, Aleksandra Pizurica UGent and Wilfried Philips UGent (2009) IEEE TRANSACTIONS ON IMAGE PROCESSING. 18(6). p.1153-1165
abstract
Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that defines the "signal of interest" and that is applicable to correlated noise. We combine the intrascale model with a Hidden Markov Tree model to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
IMAGES, HIDDEN MARKOV-MODELS, SPARSE, TRANSFORM, SCALE, hidden Markov trees (HMT), image denoising, Correlated noise
journal title
IEEE TRANSACTIONS ON IMAGE PROCESSING
IEEE Trans. Image Process.
volume
18
issue
6
pages
1153 - 1165
Web of Science type
Article
Web of Science id
000266163300001
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.2009.2017169
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1002617
handle
http://hdl.handle.net/1854/LU-1002617
date created
2010-07-05 18:15:35
date last changed
2016-12-19 15:42:12
@article{1002617,
  abstract     = {Images, captured with digital imaging devices, often contain noise. In literature, many algorithms exist for the removal of white uncorrelated noise, but they usually fail when applied to images with correlated noise. In this paper, we design a new denoising method for the removal of correlated noise, by modeling the significance of the noise-free wavelet coefficients in a local window using a new significance measure that  defines the {\textacutedbl}signal of interest{\textacutedbl} and that is applicable to correlated noise. We combine the intrascale model with a Hidden Markov Tree model  to capture the interscale dependencies between the wavelet coefficients. We propose a denoising method based on the combined model and a less redundant wavelet transform. We present results that show that the new method performs as well as the state-of-the-art wavelet-based methods, while having a lower computational complexity.},
  author       = {Goossens, Bart and Pizurica, Aleksandra and Philips, Wilfried},
  issn         = {1057-7149},
  journal      = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
  keyword      = {IMAGES,HIDDEN MARKOV-MODELS,SPARSE,TRANSFORM,SCALE,hidden Markov trees (HMT),image denoising,Correlated noise},
  language     = {eng},
  number       = {6},
  pages        = {1153--1165},
  title        = {Removal of correlated noise by modeling the signal of interest in the wavelet domain},
  url          = {http://dx.doi.org/10.1109/TIP.2009.2017169},
  volume       = {18},
  year         = {2009},
}

Chicago
Goossens, Bart, Aleksandra Pizurica, and Wilfried Philips. 2009. “Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain.” Ieee Transactions on Image Processing 18 (6): 1153–1165.
APA
Goossens, B., Pizurica, A., & Philips, W. (2009). Removal of correlated noise by modeling the signal of interest in the wavelet domain. IEEE TRANSACTIONS ON IMAGE PROCESSING, 18(6), 1153–1165.
Vancouver
1.
Goossens B, Pizurica A, Philips W. Removal of correlated noise by modeling the signal of interest in the wavelet domain. IEEE TRANSACTIONS ON IMAGE PROCESSING. 2009;18(6):1153–65.
MLA
Goossens, Bart, Aleksandra Pizurica, and Wilfried Philips. “Removal of Correlated Noise by Modeling the Signal of Interest in the Wavelet Domain.” IEEE TRANSACTIONS ON IMAGE PROCESSING 18.6 (2009): 1153–1165. Print.