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Performance of a tiled array compressive sensing spectrometer

Proceedings of SPIE - The International Society for Optical Engineering

Shields, Eric A.

A Compressive Sensing Snapshot Imaging Spectrometer (CSSIS) and its performance are described. The number of spectral bins recorded in a traditional tiled array spectrometer is limited to the number of filters. By properly designing the filters and leveraging compressive sensing techniques, more spectral bins can be reconstructed. Simulation results indicate that closely-spaced spectral sources that are not resolved with a traditional spectrometer can be resolved with the CSSIS. The nature of the filters used in the CSSIS enable higher signal-to-noise ratios in measured signals. The filters are spectrally broad relative to narrow-line filters used in traditional systems, and hence more light reaches the imaging sensor. This enables the CSSIS to outperform a traditional system in a classification task in the presence of noise. Simulation results on classifying in the compressive domain are shown. This obviates the need for the computationally-intensive spectral reconstruction algorithm.

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TYPE Conference Poster YEAR 2019
ScopusOSTI

Compressive hyperspectral imaging using total variation minimization

Proceedings of SPIE - The International Society for Optical Engineering

Lee, Dennis J.; Shields, Eric A.

Compressive sensing shows promise for sensors that collect fewer samples than required by traditional Shannon-Nyquist sampling theory. Recent sensor designs for hyperspectral imaging encode light using spectral modulators such as spatial light modulators, liquid crystal phase retarders, and Fabry-Perot resonators. The hyperspectral imager consists of a filter array followed by a detector array. It encodes spectra with less measurements than the number of bands in the signal, making reconstruction an underdetermined problem. We propose a reconstruction algorithm for hyperspectral images encoded through spectral modulators. Our approach constrains pixels to be similar to their neighbors in space and wavelength, as natural images tend to vary smoothly, and it increases robustness to noise. It combines L1 minimization in the wavelet domain to enforce sparsity and total variation in the image domain for smoothness. The alternating direction method of multipliers (ADMM) simplifies the optimization procedure. Our algorithm constrains encoded, compressed hyperspectral images to be smooth in their reconstruction, and we present simulation results to illustrate our technique. This work improves the reconstruction of hyperspectral images from encoded, multiplexed, and sparse measurements.

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TYPE Conference Poster YEAR 2018
ScopusOSTI
Results 1–25 of 26
Results 1–25 of 26