Image denoising using multiple wavelet representations and local contextual hidden Markov model

Wavelet-domain local contextual hidden Markov model (LCHMM) can exploit both the local statistics and the intrascale dependencies of wavelet coefficients at a low computational complexity. Multiple wavelet representations have excellent performance in image denoising. In this paper, combining the multiple wavelet representations with the LCHMM and using their advantages in image denoising, we propose a new image denoising algorithm, called M-LCHMM. It is simple and effective. Simulation results show that the proposed M-LCHMM can achieve the state-of-the-art image denoising performance at the low computational complexity.

[1]  Xiang-Gen Xia,et al.  Wavelet-Based Statistical Image Processing Using Hidden Markov Tree Model , 2000 .

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

[3]  Michael Elad,et al.  A generalized uncertainty principle and sparse representation in pairs of bases , 2002, IEEE Trans. Inf. Theory.

[4]  Mário A. T. Figueiredo.  Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

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

[6]  Guoliang Fan,et al.  Image denoising using a local contextual hidden Markov model in the wavelet domain , 2001, IEEE Signal Processing Letters.

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

[8]  Michael Elad,et al.  Optimally sparse representation in general (nonorthogonal) dictionaries via ℓ1 minimization , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  R.G. Baraniuk,et al.  Contextual hidden Markov models for wavelet-domain signal processing , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[10]  Guang Deng,et al.  A signal denoising algorithm based on overcomplete wavelet representations and Gaussian models , 2007, Signal Process..

[11]  Justin K. Romberg,et al.  Bayesian tree-structured image modeling using wavelet-domain hidden Markov models , 2001, IEEE Trans. Image Process..