Paper
23 June 2003 Spatially adaptive denoising using mixture modeling of wavelet coefficients
Il Kyu Eom, Yoo-shin Kim
Author Affiliations +
Proceedings Volume 5150, Visual Communications and Image Processing 2003; (2003) https://doi.org/10.1117/12.501818
Event: Visual Communications and Image Processing 2003, 2003, Lugano, Switzerland
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.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Il Kyu Eom and Yoo-shin Kim "Spatially adaptive denoising using mixture modeling of wavelet coefficients", Proc. SPIE 5150, Visual Communications and Image Processing 2003, (23 June 2003); https://doi.org/10.1117/12.501818
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KEYWORDS
Wavelets

Binary data

Wavelet transforms

Denoising

Filtering (signal processing)

Image denoising

Statistical analysis

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