Image Denoising with Trivariate Shrinkage Based on Sharp Frequency Localization Contourlet

Title:Image Denoising with Trivariate Shrinkage Based on Sharp Frequency Localization Contourlet

Volume: 9 Issue: 1

Author(s): Zhang Chi and Jin Huixia

Affiliation:

Keywords: Bayesian, contourlet, image denoising, statistical distribution, shrinkage function, trivariate distribution.

Abstract: Conventional thresholding shrinkage for denoising is designed with assumption that the coefficients in transformation domain are independent. However, in practice, natural images’ coefficients in transformation domain have significant dependencies. In this paper, we proposed a novel method for image denoising by exploring the dependencies among the coefficients. The method considered three corresponding coefficients, including the noisy coefficient, its parent coefficient and its neighbor coefficient based on the Sharp Frequency Localization Contourlet, and established a trivariate distribution model to estimate the latent coefficient. Furthermore, the shrinkage function in model is derived under the Bayesian framework. The experiment results showed that the performance of proposed method outperformed the current denoising method.

Cite this article as:

Chi Zhang and Huixia Jin, Image Denoising with Trivariate Shrinkage Based on Sharp Frequency Localization Contourlet, Recent Advances in Electrical & Electronic Engineering 2016; 9 (1) . https://dx.doi.org/10.2174/235209650901160419002722

DOI https://dx.doi.org/10.2174/235209650901160419002722	Print ISSN 2352-0965
Publisher Name Bentham Science Publisher	Online ISSN 2352-0973