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Registration of noisy images via maximum a-posteriori estimation
Sebastian Suhr, Daniel Tenbrinck, Martin Burger,
Jan Modersitzki
Institute of Mathematics and Image Computing
École Nationale Supérieure d'Ingénieurs de Caen
University of Münster
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Computer Science
Noise-to-Signal Ratio
100%
Acquisition Process
100%
Induced Problem
100%
Acquisition Time
100%
Conventional Method
100%
Engineering
Maximum a Posteriori
100%
Noisy Image
100%
Signal-to-Noise Ratio
50%
Acquisition Time
50%
Impact Noise
50%