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

Authors

  • H. Nezamabadi-pour Intelligent Data Processing Laboratory (IDPL), Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
  • M. Saeedzarandi 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.
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.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

Wavelet Based Image Denoising Based on a Mixture of Laplace Distributions

Abstract– The performance of various estimators, such as maximum a posteriori (MAP), strongly depends on correctness of the proposed model for distribution of noise-free data. Therefore, the selection of a proper model for the distribution of wavelet coefficients is very important in wavelet based image denoising. This paper presents a new image denoising algorithm based on the modeling of wave...

full text

Image Denoising with a Mixture of Gaussian Distributions with Local Parameters in Wavelet Domain

The proposed model for noise-free data distribution play an important role for maximum a posteriori (MAP) estimator. Thus, in the wavelet based image denoising, it is necessary to select a proper model for distribution of wavelet coefficients. This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each subband with a mixture of Gaussian probability ...

full text

Spatially adaptive denoising using mixture modeling of wavelet coefficients

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 eithe...

full text

Wavelet Bayes Adaptive Image Denoising

The class of natural images that we encounter in our daily life is only a small subset of the set of all possible images. This subset is called an image manifold. The Adaptive Digital Image Processing applications are becoming increasingly important and they all start with a mathematical representation of the image. In Bayesian restoration methods, the image manifold is encoded in the form of p...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume 8  issue 2

pages  289- 301

publication date 2020-04-01

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023