A novel compressive imaging model is proposed that multiplexes segments of the field of view onto an infrared focal plane array. Similar to compound imaging, our model is based on combining pixels from a surface comprising of different parts of the FOV. We formalize this superposition of pixels in a global multiplexing process reducing the number of detectors required of the FPA. We present an analysis of the Signal-to-Noise Ratio (SNR) for the full rank and compressive collection paradigms for a target detection and tracking scenario.

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) .

Authors:

Robert Muise (Lockheed Martin)

Keywords:

image compression, image reconstruction, image representation, inverse problems