A Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common Space-Invariant Blur

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A Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common Space-Invariant Blur (2001)

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by Michael Elad , Yacov Hel-Or

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@MISC{Elad01afast,
    author = {Michael Elad and Yacov Hel-Or},
    title = {A Fast Super-Resolution Reconstruction Algorithm for Pure Translational Motion and Common Space-Invariant Blur},
    year = {2001}
}

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Abstract

This paper addresses the problem of recovering a super-resolved image from a set of warped blurred and decimated versions thereof. Several algorithms have already been proposed for the solution of this general problem. In this paper, we concentrate on a special case where the warps are pure translations, the blur is space invariant and the same for all the images, and the noise is white. We exploit previous results to develop a new highly efficient super-resolution reconstruction algorithm for this case, which separates the treatment into de-blurring and measurements fusion. The fusion part is shown to be a very simple noniterative algorithm, preserving the optimality of the entire reconstruction process, in the maximum-likelihood sense. Simulations demonstrate the capabilities of the proposed algorithm.

Keyphrases

common space-invariant blur pure translational motion fast super-resolution reconstruction algorithm fusion part several algorithm maximum-likelihood sense general problem previous result efficient super-resolution reconstruction algorithm space invariant entire reconstruction process measurement fusion special case super-resolved image simple noniterative algorithm pure translation

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