In this paper, we propose a novel framework for face super-resolution based on a layered predictor network. In the first layer, multiple predictors are trained online with a dynamic-constructed training set, which is adaptively selected in order to make the trained model tailored to the testing face. When the dynamic training set is obtained, the optimum predictor can be learned based on the resampling-maximum likelihood-model. To further enhance the robustness of prediction and the smoothness of the hallucinated image, additional layers are designed to fuse multiple predictors with the fusion rule learned from the training set. Experiments fully demonstrate the effectiveness of the framework.


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    Title :

    Layered local prediction network with dynamic learning for face super-resolution


    Contributors:
    Dahua Lin, (author) / Wei Liu, (author) / Xiaoou Tang, (author)


    Publication date :

    2005-01-01


    Size :

    185187 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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