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By incorporating the priors that human face is a class of highly structured object, position-patch based face hallucination methods represent the test image patch through the same position patches of training faces by employing least square estimation or sparse coding. Due to they cannot provide unbiased approximations or ignore the influence of spatial distances between the test image patch and training...
Recently, position-patch based approaches have been proposed to replace the probabilistic graph-based or manifold learning-based models for face hallucination. In order to obtain the optimal weights of face hallucination, these approaches represent one image patch through other patches at the same position of training faces by employing least square estimation or sparse coding. However, they cannot...
Instead of using probabilistic graph based or manifold learning based models, some approaches based on position-patch have been proposed for face hallucination recently. In order to obtain the optimal weights for face hallucination, they represent image patches through those patches at the same position of training face images by employing least square estimation or convex optimization. However, they...
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