Publications

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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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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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We describe a phase retrieval method capable of recovering different phase distributions for each wavelength forming a polychromatic intensity distribution. Simulation results show reconstruction errors of a few thousandths of a wave RMS.

This diffractive optical element (DOE) LDRD is divided into two tasks. In Task 1, we develop two new DOE technologies: (1) a broad wavelength band effective anti-reflection (AR) structure and (2) a design tool to encode dispersion and polarization information into a unique diffraction pattern. In Task 2, we model, design, and fabricate a subwavelength polarization splitter. The first technology is an anti-reflective (AR) layer that may be etched into the DOE surface. For many wavelengths of interest, transmissive silicon DOEs are ideal. However, a significant portion of light (30% from each surface) is lost due to Fresnel reflection. To address this issue, we investigate a subwavelength, surface relief structure that acts as an effective AR coating. The second DOE component technology in Task 1 is a design tool to determine the optimal DOE surface relief structure that can encode the light's degree of dispersion and polarization into a unique spatial pattern. Many signals of interest have unique spatial, temporal, spectral, and polarization signatures. The ability to disperse the signal into a unique diffraction pattern would result in improved signal detection sensitivity with a simultaneous reduction in false alarm. Task 2 of this LDRD project is to investigate the modeling, design, and fabrication of subwavelength birefringent devices for polarimetric spectral sensing and imaging applications. Polarimetric spectral sensing measures the spectrum of the light and polarization state of light at each wavelength simultaneously. The capability to obtain both polarization and spectral information can help develop target/object signature and identify the target/object for several applications in NP&MC and national security.