Wavelet-based image denoising using three scales of dependency

The denoising of a natural image corrupted by the Gaussian white noise is a classical problem in image processing. A new image denoising method is proposed by using three scales of dual-tree complex wavelet coefficients. The dual-tree complex wavelet transform is well known for its approximate shift invariance and better directional selectivity, which are very important in image denoising. Experiments show that the proposed method is very competitive when compared with other existing denosing methods in the literature.

[1]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[2]  D. Donoho,et al.  Translation-Invariant De-Noising , 1995 .

[3]  T. R. Downie,et al.  The discrete multiple wavelet transform and thresholding methods , 1998, IEEE Trans. Signal Process..

[4]  Nick G. Kingsbury,et al.  The dual-tree complex wavelet transform: A new efficient tool for image restoration and enhancement , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[5]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[6]  Tien D. Bui,et al.  Translation-invariant denoising using multiwavelets , 1998, IEEE Trans. Signal Process..

[7]  Nick G. Kingsbury,et al.  Shift invariant properties of the dual-tree complex wavelet transform , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[8]  Peter N. Heller,et al.  The application of multiwavelet filterbanks to image processing , 1999, IEEE Trans. Image Process..

[9]  Kannan Ramchandran,et al.  Low-complexity image denoising based on statistical modeling of wavelet coefficients , 1999, IEEE Signal Processing Letters.

[10]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[11]  B. Silverman,et al.  Incorporating Information on Neighboring Coefficients Into Wavelet Estimation , 2001 .

[12]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[13]  I. Selesnick,et al.  Bivariate shrinkage with local variance estimation , 2002, IEEE Signal Processing Letters.

[14]  T. D. Bui,et al.  Multiwavelets denoising using neighboring coefficients , 2003, IEEE Signal Processing Letters.

[15]  T. D. Bui,et al.  Image denoising using neighbouring wavelet coefficients , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  Adam Krzyzak,et al.  Image denoising with neighbour dependency and customized wavelet and threshold , 2005, Pattern Recognit..

[17]  Balázs Kégl,et al.  Image denoising with complex ridgelets , 2007, Pattern Recognit..

[18]  Thierry Blu,et al.  A New SURE Approach to Image Denoising: Interscale Orthonormal Wavelet Thresholding , 2007, IEEE Transactions on Image Processing.

[19]  Guangyi Chen,et al.  Pattern recognition with SVM and dual-tree complex wavelets , 2007, Image Vis. Comput..