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Limits on super-resolution and how to break them

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2 Author(s)
Baker, S. ; Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA ; Kanade, T.

We analyze the super-resolution reconstruction constraints. In particular we derive a sequence of results which all show that the constraints provide far less useful information as the magnification factor increases. It is well established that the use of a smoothness prior may help somewhat, however for large enough magnification factors any smoothness prior leads to overly smooth results. We therefore propose an algorithm that learns recognition-based priors for specific classes of scenes, the use of which gives far better super-resolution results for both faces and text

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Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on  (Volume:2 )

Date of Conference: