Title :
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
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;
Conference_Titel :
Computer Science and Computational Technology, 2008. ISCSCT '08. International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3746-7
DOI :
10.1109/ISCSCT.2008.15
