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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)
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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.
Keywords
IMAGES, HIDDEN MARKOV-MODELS, SPARSE, TRANSFORM, SCALE, hidden Markov trees (HMT), image denoising, Correlated noise

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Citation

Please use this url to cite or link to this publication:

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.
@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},
}

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