Fast Detection of Compressively-Sensed IR Targets Using Stochastically Trained Least Squares and Compressed Quadratic Correlation FiltersMS60

Target detection on a high resolution midwave infrared focal plane array is cost prohibitive in some applications. But, due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. As the most probable coefficient indices of the support set of the infrared image patches could be learnt from the training data, we develop STLS for MWIR image reconstruction. Using the same measurement matrix as in STLS, we construct CQCF for compressed infrared target detection. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods but at a fraction of the execution time.

This presentation is part of Minisymposium “MS60 - Computational and Compressive Imaging Technologies and Applications (3 parts)
organized by: Robert Muise (Lockheed Martin) , Richard Baraniuk (Rice University) .

Brian Millikan (University of Central Florida)