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Proceedings Paper

Stochastic wavelet-based image modeling using factor graphs and its application to denoising
Author(s): Shu Xiao; Igor V. Kozintsev; Kannan Ramchandran
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Paper Abstract

In this work, we introduce a hidden Markov field model for wavelet image coefficients within a subband and apply it to the image denoising problem. Specifically, we propose to model wavelet image coefficients within subbands as Gaussian random variables with parameters determined by the underlying hidden Markov process. Our model is inspired by the recent Estimation-Quantization (EQ) image coder and its excellent performance in compression. To reduce the computational complexity we apply a novel factor graph framework to combine two 1-D hidden Markov chain models to approximate a hidden Markov Random field (HMRF) model. We then apply the proposed models for wavelet image coefficients to perform an approximate Minimum Mean Square Error (MMSE) estimation procedure to restore an image corrupted by an additive white Gaussian noise. Our results are among the state-of-the-art in the field and they indicate the promise of the proposed modeling techniques.

Paper Details

Date Published: 19 April 2000
PDF: 9 pages
Proc. SPIE 3974, Image and Video Communications and Processing 2000, (19 April 2000); doi: 10.1117/12.382988
Show Author Affiliations
Shu Xiao, Univ. of Illinois/Urbana-Champaign (United States)
Igor V. Kozintsev, Univ. of Illinois/Urbana-Champaign (United States)
Kannan Ramchandran, Univ. of California/Berkeley (United States)

Published in SPIE Proceedings Vol. 3974:
Image and Video Communications and Processing 2000
Bhaskaran Vasudev; T. Russell Hsing; Andrew G. Tescher; Robert L. Stevenson, Editor(s)

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