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Proceedings Paper

Spatially adaptive denoising using mixture modeling of wavelet coefficients
Author(s): Il Kyu Eom; Yoo-shin Kim
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Paper Abstract

A wavelet coefficient is generally classified into two categories: significant (large) and insignificant (small). Therefore, each wavelet coefficient is efficiently modelled as a random variable of a Gaussian mixture distribution with unknown parameters. In this paper, we propose an image denoising method by using mixture modelling of wavelet coefficients. The coefficient is classified as either noisy or clean by using proper threshold [2]. Based on this classification, binary mask value that takes an important role to suppress noise is produced. The probability of being clean signal is estimated by a set of mask values. Then we apply this probability to design Wiener filter to reduce noise and also develop the method of selecting windows of different sizes around the coefficient. Despite the simplicity of our method, experimental results show that our method outperforms other critically sampled wavelet denoising schemes.

Paper Details

Date Published: 23 June 2003
PDF: 8 pages
Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); doi: 10.1117/12.501818
Show Author Affiliations
Il Kyu Eom, Miryang National Univ. (South Korea)
Yoo-shin Kim, Pusan National Univ. (South Korea)

Published in SPIE Proceedings Vol. 5150:
Visual Communications and Image Processing 2003
Touradj Ebrahimi; Thomas Sikora, Editor(s)

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