A Bayesian Approach to Alignment-Based Image Hallucination

In most image hallucination work, a strong assumption is held that images can be aligned to a template on which the prior of high-res images is formulated and learned. Realizing that one template can hardly generalize to all images of an object such as faces due to pose and viewpoint variation as well as occlusion, we propose an example-based prior distribution via dense image correspondences. We introduce a Bayesian formulation based on an image prior that can implement different effective behaviors based on the value of a single parameter. Using faces as examples, we show that our system outperforms the prior state of art.

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