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Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation

Publish Year: 1399
Type: Journal paper
Language: English
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Document National Code:

JR_JADM-8-2_012

Index date: 22 July 2020

Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation abstract

Removing noise from images is a challenging problem in digital image processing. This paper presents an image denoising method based on a maximum a posteriori (MAP) density function estimator, which is implemented in the wavelet domain because of its energy compaction property. The performance of the MAP estimator depends on the proposed model for noise-free wavelet coefficients. Thus in the wavelet based image denoising, selecting a proper model for wavelet coefficients is very important. In this paper, we model wavelet coefficients in each sub-band by heavy-tail distributions that are from scale mixture of normal distribution family. The parameters of distributions are estimated adaptively to model the correlation between the coefficient amplitudes, so the intra-scale dependency of wavelet coefficients is also considered. The denoising results confirm the effectiveness of the proposed method.

Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation Keywords:

Statistical Wavelet-based Image Denoising using Scale Mixture of Normal Distributions with Adaptive Parameter Estimation authors

M. Saeedzarandi

Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

H. Nezamabadi-pour

Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

S. Saryazdi

Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.