We propose an efficient hybrid Fourier-Wavelet domain hidden Markov model. Fourier regularized deconvolution algorithm performs noise regularization using scalar shrinkage in the Fourier domain. The Fourier shrinkage exploit the Fourier transform's economical representation of the colored noise inherent in deconvolution, whereas the hidden Markov tree (HMT) model captures the key features of the joint probability density of the wavelet coefficients of real-world data. The simplified model specifies the HMT parameters; and makes the model suitable for real-world applications.
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