Non-subsampled contourlet domain image de-noising employing joint statistical model

Article type: Research Article

Authors: Fan, Fengfei | Gai, Shan^{; *}

Affiliations: School of Information Engineering, Nanchang Hangkong University, Nanchang, China

Correspondence: [*] Corresponding author. Shan Gai, School of Information Engineering, Nanchang Hangkong University, Nanchang, China. Tel.: +86 15713538455; E-mail: gaishan@nchu.edu.cn.

Abstract: A new de-noising algorithm by using the Laplace model and the Normal Inverse Gauss model based on the non-subsampled contourlet transform is proposed. Firstly, the sub-band coefficients of non-subsampled transform are fitted by using the joint statistical model which can capture the texture information well of the natural image. Secondly, the new adjustment factor is introduced to improve the coefficients fitting accuracy of the joint statistical model. Finally, the new parameter estimation algorithm is proposed under the Bayesian estimation framework. The experimental results show that the proposed algorithm obtains better visual effects and de-noising results compared with the state-of-art de-noising algorithms.

Keywords: Image de-noising, non-subsampled contourlet transform, laplace model, normal Inverse Gaussian model, bayesian estimation

DOI: 10.3233/JIFS-17434

Journal: Journal of Intelligent & Fuzzy Systems, vol. 34, no. 1, pp. 721-731, 2018