In this paper we propose a new hierarchical non stationary image prior for image restoration. This prior captures the directional edges using a continuous model and regularizes accordingly the restored images. In addition, the corresponding generative graphical model does not contain cycles, thus learning this model is easy and fast. Based on this prior image model, a maximum a posteriori (MAP) estimation algorithm is derived. Numerical experiments are provided that demonstrate the advantages of the proposed non stationary model as compared with algorithms that use stationary models.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Maximum a posteriori image restoration based on a new directional continuous edge image prior


    Contributors:
    Chantas, G. (author) / Galatsanos, N. (author) / Likas, A. (author)


    Publication date :

    2005-01-01


    Size :

    457140 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Maximum a Posteriori Image Restoration Based on a New Directional Continuous Edge Image Prior

    Chantas, G. / Galatsanos, N. / Likas, A. | British Library Conference Proceedings | 2005



    Maximum a posteriori image registration/motion estimation

    Oshman, Yaakov / Menis, Baruch | AIAA | 1994


    Traffic Image Segmentation Based on Maximum Posteriori Mutual Information

    Cao, L. / Shi, Z.-k. / Chen, W. | British Library Conference Proceedings | 2009


    Traffic image segmentation based on maximum posteriori mutual information

    Cao, Li / Shi, Zhong-ke / Chen, Wen | IEEE | 2009