We present a theoretical study of the recovery of images x from noisy data y by minimizing a regularized cost-function F(x,y)=/spl Psi/(x,y)+/spl alpha//spl Phi/(x), where /spl Psi/ is a data-fidelity term, /spl Phi/ is a smooth regularisation term and /spl alpha/>0 is a parameter. Generally /spl Psi/ is a smooth function; only a few papers make an exception. Non-smooth data-fidelity terms are avoided in image processing. In spite of this, we consider both smooth and non-smooth data-fidelity terms. Our ambition is to catch essential features exhibited by the local minimizers of F in relation with the smoothness of /spl Psi/. Cost-functions with non-smooth data-fidelity exhibit a strong mathematical property which can be used in various ways. We then construct a cost-function allowing aberrant data to be detected and selectively smoothed. The obtained results advocate the use of non-smooth data-fidelity terms.


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    Title :

    Image restoration by minimizing objective functions with nonsmooth data-fidelity terms


    Contributors:


    Publication date :

    2001-01-01


    Size :

    938195 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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