Image Denoising with Contourlet Transform Based on PCA

Author

Sun Lin-li ; Li Yan ; Zheng Jian-ming

Author_Institution

Xi´an Univ. of Technol., Xian, China

Volume

1

fYear

2008

fDate

20-22 Dec. 2008

Firstpage

31

Lastpage

33

Abstract

This paper adopted multiscale geometry method, distilled the principal component from the image after Contourlet transform, lowered the dimension of the high frequency subdomains, eliminated the noise by minimum variance cost function. The entire arithmetic without estimate noise, compared to Contourlet hard threshod denoising and wavelet hard threshod denoising, PSNR increased 1 dB, the denoising effect is better than other methods in the experiment.

Keywords

image denoising; principal component analysis; wavelet transforms; contourlet hard threshod denoising; contourlet transform; image denoising; minimum variance cost function; multiscale geometry method; noise eliminated; principal component analysis; wavelet hard threshod denoising; Arithmetic; Cost function; Covariance matrix; Filter bank; Image denoising; Image reconstruction; Noise reduction; PSNR; Paper technology; Principal component analysis; Contourlet transform; PCA (principal component analysis); denoise; multiscale geometry analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on

Conference_Location

Shanghai

Print_ISBN

978-1-4244-3746-7

Type

conf

DOI

10.1109/ISCSCT.2008.15

Filename

4731367