Image Denoising Based on Bivariate Distribution

Ping Zhao,Chun Zhao,Xingyu Zhao

doi:10.3390/sym12111909

Ping Zhao, Chun Zhao + Show 1 more

Open Access

https://doi.org/10.3390/sym12111909

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Journal: Symmetry	Publication Date: Nov 20, 2020
Citations: 1	License type: CC BY 4.0

Affiliation: Tianjin Normal University, Beijing Jiaotong University

Abstract

The literature has shown that the performance of the de-noising algorithm was greatly influenced by the dependencies between wavelet coefficients. In this paper, the bivariate probability density function (PDF) was proposed which was symmetric, and the dependencies between the coefficients were considered. The bivariate Cauchy distribution and the bivariate Student’s distribution are special cases of the proposed bivariate PDF. One of the parameters in the probability density function gave the estimation method, and the other parameter can take any real number greater than 2. The algorithm adopted a maximum a posteriori estimator employing the dual-tree complex wavelet transform (DTCWT). Compared with the existing best results, the method is faster and more efficient than the previous numerical integration techniques. The bivariate shrinkage function of the proposed algorithm can be expressed explicitly. The proposed method is simple to implement.

Highlights

Image denoising is very important in many image processing applications
In [16], a denoising speckle algorithm was proposed using a heavy tailed Levy distribution based on dual-tree complex wavelet transform (DTCWT) for the ultrasound images
A bivariate symmetric probability density function (PDF) was proposed, which utilizes the dependence of DTCWT wavelet coefficients of each subband

Summary

Introduction

Image denoising is very important in many image processing applications. The methods of de-noising using wavelet transform have been widely proposed. Chang et al proposed a subband adaptive method based on the generalized Gaussian distribution (GGD) model in [1]. In [5], a threshold estimation denoising method based on double density wavelet transform (DDWT) was proposed. Ranjani and Thiruvengadam in [14,15] proposed a despeckling algorithm using the bivariate Cauchy probability density function (PDF) by the DTCWT subbands, respectively. In [16], a denoising speckle algorithm was proposed using a heavy tailed Levy distribution based on DTCWT for the ultrasound images. A bivariate symmetric probability density function (PDF) was proposed, which utilizes the dependence of DTCWT wavelet coefficients of each subband.

Proposed Algorithm

Experimental Results

Conclusions