The formulation and accuracy of the linear prediction involves the statistical model of a natural image and the minimization of the prediction error by considering the “minimum mean squares error” (MMSE). This chapter discusses the second‐order statistical model of natural image and the basic of MMSE optimization‐based image interpolation method. New Edge‐Directed Interpolation (NEDI) method assumes the image model assumptions: natural image is a locally stationary second‐order Gaussian process; and low‐resolution and high‐resolution images have similar second‐order statistics in corresponding local patches. One of the reasons leading to the covariance mismatch of the NEDI is the application of fixed window structure, both size and shape. As a result, one of the remedies is to modify the window to enclose the major feature only, such as to ensure that the low‐resolution image and high‐resolution image have similar covariance structures.


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    Title :

    Covariance‐Based Interpolation


    Contributors:


    Publication date :

    2019-02-19


    Size :

    52 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English






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