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.
Layered local prediction network with dynamic learning for face super-resolution
2005-01-01
185187 byte
Conference paper
Electronic Resource
English
Layered Local Prediction Network with Dynamic Learning for Face Super-resolution
British Library Conference Proceedings | 2005
|Learning-Based Super-Resolution of 3D Face Model
British Library Conference Proceedings | 2005
|Learning-based super-resolution of 3D face model
IEEE | 2005
|British Library Conference Proceedings | 2006
|