Image denoising algorithm via doubly local Wiener filtering with directional windows in wavelet domain

Local Wiener filtering in the wavelet domain is an effective image denoising method of low complexity. In this letter, we propose a doubly local Wiener filtering algorithm, where the elliptic directional windows are used for different oriented subbands in order to estimate the signal variances of noisy wavelet coefficients, and the two procedures of local Wiener filtering are performed on the noisy image. The experimental results show that the proposed algorithm improves the denoising performance significantly.

[1]  Richard G. Baraniuk,et al.  Improved wavelet denoising via empirical Wiener filtering , 1997, Optics & Photonics.

[2]  Il Kyu Eom,et al.  Wavelet-based denoising with nearly arbitrarily shaped windows , 2004, IEEE Signal Process. Lett..

[3]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[4]  Michael T. Orchard,et al.  Spatially adaptive image denoising under overcomplete expansion , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

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

[6]  M. Kazubek,et al.  Wavelet domain image denoising by thresholding and Wiener filtering , 2003, IEEE Signal Processing Letters.

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

[8]  Martin J. Wainwright,et al.  Image denoising using scale mixtures of Gaussians in the wavelet domain , 2003, IEEE Trans. Image Process..

[9]  X. Xia,et al.  Image denoising using a local contextual hidden Markov model in the wavelet domain , 2001, IEEE Signal Process. Lett..