Aliasing artifacts in images are visually very disturbing. Therefore, most imaging devices apply a low-pass filter before sampling. This removes all aliasing from the image, but it also creates a blurred image. Actually, all the image information above half the sampling frequency is removed. In this paper, we present a new method for the reconstruction of a high resolution image from a set of highly undersampled and thus aliased images. We use the information in the entire frequency spectrum, including the aliased part, to create a sharp, high resolution image. The unknown relative shifts between the images are computed using a subspace projection approach. We show that the projection can be decomposed into multiple projections onto smaller subspaces. This allows for a considerable reduction of the overall computational complexity of the algorithm. A high resolution image can then be reconstructed from the registered low resolution images. Simulation results show the validity of our algorithm.


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    Title :

    Super-resolution from highly undersampled images


    Contributors:
    Vandewalle, P. (author) / Sbaiz, L. (author) / Vetterli, M. (author) / Sustrunk, S. (author)


    Publication date :

    2005-01-01


    Size :

    194006 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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