We present a polarized dual Single Pixel Camera (SPC) operating in the Short Wave Infrared (SWIR) spectral range that uses a total variation based reconstruction method to reconstruct polarized images from an ensemble of compressed measurements. Walsh-Hadamard matrices are used for generating pseudo-random measurements which speed up the reconstruction and enable reconstruction of high resolution images. The system combines a Digital Micromirror Device (DMD), two nearly identical InGaAs photodiodes and two polarization filters. Roughly half of the DMD-mirrors are oriented toward the first photodiode and the complementary DMD-mirrors are oriented toward the second photodiode. Total variation based reconstruction strategies have been implemented, and evaluated on both simulated compressed measurements, and real outdoor scenes using the developed system.
Raman spectroscopy is an efficient method for detection of explosives even in small quantities. A laser can be combined with a Coded Aperture Snapshot Spectral Imaging (CASSI) system to collect Raman spectra from a surface at stand-off distances. The CASSI-system decrease the data collection time but instead increase the reconstruction time for the Raman image. Reconstruction of Raman spectra from an ensemble of compressed sensing measurements using standard reconstruction methods such as Total Variation (TV) is rather time consuming and limits the application domain for the technique. Novel machine learning approaches such as Deep Learning (DL) has lately been applied to reconstruction problems. We evaluate our earlier developed DL approach for reconstruction of Raman spectra from an ensemble of measurements formulated as a regression problem. The DL network is trained by minimizing a loss-function which is composed of two components: a reconstruction error and a re-projection error. The evaluated method is trained on simulated data where the training data has been generated using a transfer function. The transfer function has been developed to mimic the optical properties of a CASSI system. The DL network has been trained on different training sets with different levels of background noise, different number of materials in the scene and different spatial configurations of the materials in the scene. The reconstruction results using the DL network has been qualitatively evaluated on simulated data and the results are also compared to the Two-Step Iterative Shrinkage/Thresholding (TwIST) algorithm in terms of reconstruction quality and computation time. The reconstruction time for the DL are orders of magnitude lower than for TwIST without reducing the quality of the reconstructed Raman spectra.
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