Super-resolution imaging is to overcome the inherent limitations of image acquisition to create high-resolution images from their low-resolution counterparts. In this paper, a novel state-space approach is proposed to incorporate the temporal correlations among the low-resolution observations into the framework of the Kalman filtering. The proposed approach exploits both the temporal correlations information among the high-resolution images and the temporal correlations information among the low-resolution images to improve the quality of the reconstructed high-resolution sequence. Experimental results show that the proposed framework is superior to bi-linear interpolation, bi-cubic spline interpolation and the conventional Kalman filter approach, due to the consideration of the temporal correlations among the low-resolution images.


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    Titel :

    A new state-space approach for super-resolution image sequence reconstruction


    Beteiligte:
    Jing Tian, (Autor:in) / Kai-Kuang Ma, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    266663 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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