In this paper, we consider the super-resolution image reconstruction problem. We propose a Markov chain Monte Carlo (MCMC) approach to find the maximum a posterior probability (MAP) estimation of the unknown high-resolution image. Firstly, Gaussian Markov random field (GMRF) is exploited for modeling the prior probability distribution of the unknown high-resolution image. Then, a MCMC technique (in particular, the Gibbs sampler) is introduced to generate samples from the posterior probability distribution to compute the MAP estimation of the unknown high-resolution image, which is obtained as the mean of the samples. Moreover, we derive a bound on the convergence time of the proposed MCMC approach. Finally, the experimental results are presented to verify the superior performance of the proposed approach and the validity of the proposed bound.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A MCMC approach for Bayesian super-resolution image reconstruction


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


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    132592 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A MCMC Approach for Bayesian Super-Resolution Image Reconstruction

    Tian, J. / Ma, K.-K. | British Library Conference Proceedings | 2005


    Single Image Super-resolution Reconstruction Method

    HongJiu, T. / Junfei, R. / Zude, Z. | British Library Online Contents | 2004


    A Bayesian MCMC Approach to Study the Safety of Vessel Traffic

    Yin, Fengyang / Mou, Junmin / Qiu, Jianhua | ASCE | 2011


    Arbitrary scale image super-resolution reconstruction method

    Duan, Rongcheng / Wang, Haoyue / Zhao, Siqi | IEEE | 2024


    Image acquisition modeling for super-resolution reconstruction

    Gevrekci, M. / Gunturk, B.K. | IEEE | 2005