In this paper, we describe an algorithm for identifying a parametrically described blur based on kurtosis minimization. Using different choices for the parameters of the blur, the noisy blurred image is restored using Wiener filter. We use the kurtosis as a measurement of the quality of the restored image. From the set of the candidate deblurred images, the one with the minimum kurtosis is selected. The proposed technique is tested in a simulated experiment on a variety of blurs including atmospheric turbulence blurs, Gaussian blurs, and out-of-focus blurs. The proposed approach is also tested on real blurred images. Moreover, we test the performance when a wrong blur model is given. Our experiments show that the kurtosis minimization measurements match well with methods that maximize PSNR.


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    Title :

    Blur identification based on kurtosis minimization


    Contributors:
    Dalong Li, (author) / Mersereau, R.M. (author) / Simske, S. (author)


    Publication date :

    2005-01-01


    Size :

    336541 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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