Product | Iterative Identification and Restoration of Images
To account for this uncertainty, we model the point-spread function as the sum of a known deterministic component and an unknown random one. Such an approach has been studied before, but the problem of estimating the parameters of the restoration filter to our knowledge has not been addressed systematically.
We propose an approach based on a Gaussian statistical assumption and derive an iterative, expectation—maximization algorithm that simultaneously restores the image and estimates the required filter parameters. We obtain two versions of the algorithm based on two different models for the statistics of the image. The computations are performed in the discrete Fourier transform domain; thus they are computationally efficient even for large images.
We examine the convergence properties of the resulting estimators and evaluate their performance experimentally. Express 14 5 Ahmad Abu-Naser, Nikolas P. Galatsanos, Miles N. Wernick, and Dan Schonfeld J.
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A 15 9 Vadim Loyev and Yitzhak Yitzhaky Appl. Tewfik and H. Garnaoui J. A 8 7 Katsaggelos Appl. You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only.
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Login or Create Account. The minimum detail energies occur at the optimum PSF size. Having accurately estimated the PSF, the paper also proposes a fast double updating algorithm for improving the quality of the restored image. This is achieved by the least squares minimization of a system of linear equations that minimizes some error functions derived from the blurred image. Moreover, a technique is also proposed to improve the sharpness of the deconvolved images, by constrained maximization of some of the detail wavelet packet energies. Simulation results of several examples have verified that the proposed technique manages to yield a sharper image with higher PSNR than classical approaches.
Cite this paper M. Fahmy, G. Raheem, U.
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