Abstract
We propose an efficient, hybrid Fourier-Contourlet regularized deconvolution (ForCorRD) algorithm that performs noise regularization via scalar shrinkage in both the Fourier and Contourlet domains. It is based on and more efficient to the famous Fourier-Wavelet regularized deconvolution (ForWaRD) algorithm. The Fourier shrinkage exploits the Fourier transform's economical representation of the colored noise inherent in deconvolution, whereas the contourlet shrinkage exploits the contourlet domain's economical representation of piecewise smooth signals and images. Like the ForWaRD algorithm, ForCoRD is also applicable to all ill-conditioned deconvolution problems. In the same experiment condition for nature images' debluring, we prove that ForCoRD outperforms ForWaRD's in terms of both visual quality and PSNR performance.
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