@article{luo_kudenov_2016, title={Neural network calibration of a snapshot birefringent Fourier transform spectrometer with periodic phase errors}, volume={24}, ISSN={["1094-4087"]}, DOI={10.1364/oe.24.011266}, abstractNote={Systematic phase errors in Fourier transform spectroscopy can severely degrade the calculated spectra. Compensation of these errors is typically accomplished using post-processing techniques, such as Fourier deconvolution, linear unmixing, or iterative solvers. This results in increased computational complexity when reconstructing and calibrating many parallel interference patterns. In this paper, we describe a new method of calibrating a Fourier transform spectrometer based on the use of artificial neural networks (ANNs). In this way, it is demonstrated that a simpler and more straightforward reconstruction process can be achieved at the cost of additional calibration equipment. To this end, we provide a theoretical model for general systematic phase errors in a polarization birefringent interferometer. This is followed by a discussion of our experimental setup and a demonstration of our technique, as applied to data with and without phase error. The technique's utility is then supported by comparison to alternative reconstruction techniques using fast Fourier transforms (FFTs) and linear unmixing.}, number={10}, journal={OPTICS EXPRESS}, author={Luo, David and Kudenov, Michael W.}, year={2016}, month={May} }
 @article{maione_luo_miskiewicz_escuti_kudenov_2016, title={Spatially heterodyned snapshot imaging spectrometer}, volume={55}, ISSN={["2155-3165"]}, DOI={10.1364/ao.55.008667}, abstractNote={Snapshot hyperspectral imaging Fourier transform (SHIFT) spectrometers are a promising technology in optical detection and target identification. For any imaging spectrometer, spatial, spectral, and temporal resolution, along with form factor, power consumption, and computational complexity are often the design considerations for a desired application. Motivated by the need for high spectral resolution systems, capable of real-time implementation, we demonstrate improvements to the spectral resolution and computation trade-space. In this paper, we discuss the implementation of spatial heterodyning, using polarization gratings, to improve the spectral resolution trade space of a SHIFT spectrometer. Additionally, we employ neural networks to reduce the computational complexity required for data reduction, as appropriate for real-time imaging applications. Ultimately, with this method we demonstrate an 87% decrease in processing steps when compared to Fourier techniques. Additionally, we show an 80% reduction in spectral reconstruction error and a 30% increase in spatial fidelity when compared to linear operator techniques.}, number={31}, journal={APPLIED OPTICS}, author={Maione, Bryan D. and Luo, David and Miskiewicz, Matthew and Escuti, Michael and Kudenov, Michael W.}, year={2016}, month={Nov}, pages={8667–8675} }
 @article{maione_luo_kudenov_escuti_miskiewicz_2014, title={Birefringent snapshot imaging spatial heterodyne spectrometer}, volume={9099}, ISSN={["1996-756X"]}, DOI={10.1117/12.2049726}, abstractNote={High speed spectral imaging is useful for a variety of tasks spanning industrial monitoring, target detection, and chemical identification. To better meet these needs, compact hyperspectral imaging instrumentation, capable of high spectral resolution and real-time data acquisition and processing, are required. In this paper, we describe the first snapshot imaging spatial heterodyne Fourier transform spectrometer based on birefringent crystals and polarization gratings. This includes details about its architecture, as well as our preliminary proof of concept. Finally, we discuss details related to the calibration of the sensor, including our preliminary investigations into high speed data reconstruction and calibration using neural networks. With such an approach, it may be feasible to reconstruct and calibrate an entire interferogram cube in one step with minimal Fast Fourier Transform (FFT) processing.}, journal={POLARIZATION: MEASUREMENT, ANALYSIS, AND REMOTE SENSING XI}, author={Maione, Bryan D. and Luo, David A. and Kudenov, Michael W. and Escuti, Michael J. and Miskiewicz, Matthew N.}, year={2014} }