The objective of the work presented is the super-resolution restoration of a set of images, and we investigate the use of learnt image models within a generative Bayesian framework. It is demonstrated that restoration of far higher quality than that determined by classical maximum likelihood estimation can be achieved by either constraining the solution to lie on a restricted sub-space, or by using the sub-space to define a spatially varying prior. This sub-space can be learnt from image examples. The methods are applied to both real and synthetic images of text and faces, and results are compared to R.R. Schultz and R.L. Stevenson's (1996) MAP estimator. We consider in particular images of scenes for which the point-to-point mapping is a plane projective transformation which has 8 degrees of freedom. In the real image examples, registration is obtained from the images using automatic methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Super-resolution from multiple views using learnt image models


    Beteiligte:
    Capel, D. (Autor:in) / Zisserman, A. (Autor:in)


    Erscheinungsdatum :

    2001-01-01


    Format / Umfang :

    1793905 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Super-Resolution from Multiple Views Using Learnt Image Models

    Capel, D. / Zisserman, A. / IEEE | British Library Conference Proceedings | 2001


    Non-parametric image super-resolution using multiple images

    Gupta, M.D. / Rajaram, S. / Petrovic, N. et al. | IEEE | 2005


    Non-Parametric Image Super-Resolution using Multiple Images

    Gupta, M. D. / Rajaram, S. / Petrovic, N. et al. | British Library Conference Proceedings | 2005


    Bayesian video matting using learnt image priors

    Apostoloff, N. / Fitzgibbon, A. | IEEE | 2004


    Bayesian Video Matting Using Learnt Image Priors

    Apostoloff, N. / Fitzgibbon, A. / IEEE Computer Society | British Library Conference Proceedings | 2004