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2002
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Image restoration under wavelet-domain priors: an expectation-maximization approach

10 years 5 months ago
Image restoration under wavelet-domain priors: an expectation-maximization approach
This paper describes an expectation-maximization (EM) algorithm for wavelet-based image restoration (deconvolution). The observed image is assumed to be a convolved (e.g., blurred) and noisy version of the original image. Regularization is achieved by using a complexity penalty/prior in the wavelet domain, taking advantage of the well known sparsity of wavelet representations. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator in the discrete Fourier domain. The algorithm alternates between an FFT-based E-step and a DWT-based M-step, resulting in a very efficient iterative process requiring ?? ?? ? operations per iteration (where ? stands for the numper of pixels). The algorithm, which also estimates the noise variance, is called WAFER, standing for Wavelet and Fourier EM Restoration. The conditions for convergence of the proposed algorithm are also presented....
Robert D. Nowak, Mário A. T. Figueiredo
Added 24 Oct 2009
Updated 27 Oct 2009
Type Conference
Year 2002
Where ICIP
Authors Robert D. Nowak, Mário A. T. Figueiredo
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