DocumentCode :
231721
Title :
De-noising of SAR images based on Wavelet-Contourlet domain and PCA
Author :
Jing Fang ; Dong Wang ; Yang Xiao ; Ajay Saikrishna, D.
Author_Institution :
Inst. of Inf. Sci., Beijing Jiaotong Univ., Beijing, China
fYear :
2014
fDate :
19-23 Oct. 2014
Firstpage :
942
Lastpage :
945
Abstract :
After analyzing the speckle model of SAR, a SAR image de-noising method based on Wavelet-Contourlet transform and principal component analysis is presented. Compared with Wavelet transform and Contourlet transform, Wavelet-Contourlet transform can express images more sparsely and obtain image structure better. Most of the existing methods for image de-noising rely on accurate estimation of noise variance. However, the estimation of noise variance is very difficult in Wavelet-Contourlet domain. Propose a new method for SAR image de-noising based on Wavelet-Contourlet transform and principal component analysis. Simulation results also corroborate that the proposed algorithm is efficient and performs significantly better in reducing the speckle noise, obtaining a higher peak signal-to-noise ratio, retaining the image details, and improving the visual effect.
Keywords :
image denoising; principal component analysis; radar imaging; synthetic aperture radar; wavelet transforms; PCA; SAR image denoising; noise variance estimation; principal component analysis; signal-to-noise ratio; speckle model; speckle noise; synthetic aperture radar; wavelet-contourlet domain; Noise; Noise reduction; Principal component analysis; Speckle; Synthetic aperture radar; Wavelet transforms; Contourlet; Principal Component Analysis; SAR Image De-noising; Wavelet-Contourlet;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing (ICSP), 2014 12th International Conference on
Conference_Location :
Hangzhou
ISSN :
2164-5221
Print_ISBN :
978-1-4799-2188-1
Type :
conf
DOI :
10.1109/ICOSP.2014.7015143
Filename :
7015143
Link To Document :
بازگشت