Abstract
We present a scalable compressive imager that employs information-optimal measurements capable of detecting and classifying two or more targets in natural backgrounds. Measurements are optimized using Cauchy-Schwarz mutual information that bounds the probability of error.
© 2016 Optical Society of America
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