Plasmonic Spectral Imaging using Algorithmic Data Compression and Recovery Techniques

W. Y. Jang, Z. Ku, J. Park, A. Urbas, and M. Noyola
University of Dayton Research Institute / AFRL, Ohio, United States

Keywords: plasmonic, infrared, sensor, compression, recovery

We report the progress on developing the next generation IR imaging strategy based on plasmonic sensor incorporated by the data-efficient sensing algorithm with compression and recovery techniques. The surface plasmonic structure has been reported as a spectral tuning element for IR imaging application because of its ability to shape the sensor’s spectral sensitivity by controlling the interaction of light when coupled to the sensor structure. The algorithm termed the band-compressive algorithmic spectrometry (BAS) was incorporated in the design of plasmonic IR sensor to find sensor’s minimum spectral requirements associated with signatures of interest. Key concept behind the BAS is the compressive spectral sensing, which effectively compresses the number of required plasmonic spectral sensitivities thereby finding a minimum and essential bandset to be used for recovering signatures of interest. The recovery is done by linearly synthesizing a collection of raw plasmonic output images with a set of weights prescribed by the BAS. This synthetic image reconstructs radiations from targets at a specific wavelength. Recently a plasmonic structure was modeled by using a square coaxial aperture array and was coupled on a state of the art HgCdTe sensor platform operating in the longwave IR.