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Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
In this paper, we consider the problem of super-resolving a human face video by a very high (×16) zoom factor. Inspired by recent literature on hallucination and examplebased learning, we formulate this task using a graphical model that encodes 1) spatio-temporal consistencies, and 2) image formation & degradation processes. A video database of facial expressions is used to learn a domainspecific prior for high-resolution videos. The problem is posed as one of probabilistic inference, in whichdoi:10.1109/cvpr.2004.1315157 fatcat:2l6mwzfrkre3xp3haptwuukrp4