Hybrid Fourier-wavelet image denoising

被引:19
作者
Jiang, S. [1 ]
Hao, X. [1 ]
机构
[1] N Univ China, Natl Key Lab Elect Measurement Technol, Taiyuan 030051, Peoples R China
关键词
D O I
10.1049/el:20071417
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Transform-domain image denoising methods assume that the original signal can be sparsely represented in the transform domain, but none of the orthogonal transforms can achieve sparse representation for all images. Proposed is a hybrid Fourier-wavelet denoising method to overcome this shortcoming. Experimental results show that the proposed algorithm improves denoising performance efficiently.
引用
收藏
页码:1081 / 1082
页数:2
相关论文
共 8 条
[1]   Efficient wavelet-based image denoising algorithm [J].
Cai, ZH ;
Cheng, TH ;
Lu, C ;
Subramanian, KR .
ELECTRONICS LETTERS, 2001, 37 (11) :683-685
[2]   IDEAL SPATIAL ADAPTATION BY WAVELET SHRINKAGE [J].
DONOHO, DL ;
JOHNSTONE, IM .
BIOMETRIKA, 1994, 81 (03) :425-455
[3]   Wavelet threshold estimators for data with correlated noise [J].
Johnstone, IM ;
Silverman, BW .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1997, 59 (02) :319-351
[4]   A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding [J].
Luisier, Florian ;
Blu, Thierry ;
Unser, Michael .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2007, 16 (03) :593-606
[5]   Low-complexity image denoising based on statistical modeling of wavelet coefficients [J].
Mihçak, MK ;
Kozintsev, I ;
Ramchandran, K ;
Moulin, P .
IEEE SIGNAL PROCESSING LETTERS, 1999, 6 (12) :300-303
[6]   Image denoising using scale mixtures of Gaussians in the wavelet domain [J].
Portilla, J ;
Strela, V ;
Wainwright, MJ ;
Simoncelli, EP .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2003, 12 (11) :1338-1351
[7]   Bivariate shrinkage with local variance estimation [J].
Sendur, L ;
Selesnick, IW .
IEEE SIGNAL PROCESSING LETTERS, 2002, 9 (12) :438-441
[8]  
TRETTER SA, 1976, INTRO DISCRETE TIME