High-throughput hyperspectral infrared camera

Mooney, Jonathan Martin; Vickers, Virgil E.; An, Myoung; Brodzik, Andrzej K.

doi:10.1364/josaa.14.002951

Cited by 82 publications

(43 citation statements)

References 12 publications

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“…great importance in many civilian and military applications, such as night vision, target detection and tracking, thermal remote sensing for atmospheric pollution, drug monitoring, and chemical sensing and detection for biological/chemical warfare [1][2]. Current state-of-the-art infrared detection and sensing technology is based on mercury cadmium telluride (MCT) material systems.…”

mentioning

confidence: 99%

Low-bias, high-temperature operation of an InAs–InGaAs quantum-dot infrared photodetector with peak-detection wavelength of 11.7μm

Vaillancourt

Stintz

Meisner

et al. 2009

Infrared Physics & Technology

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mentioning

confidence: 99%

Low-bias, high-temperature operation of an InAs–InGaAs quantum-dot infrared photodetector with peak-detection wavelength of 11.7μm

Vaillancourt

Stintz

Meisner

et al. 2009

Infrared Physics & Technology

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“…Further, the fact that the spectral image data cube is three-dimensional, while available detector arrays are two-dimensional results in either a need for scanning (which increases the overall acquisition time) or in the tiling of the detector array with multiple two-dimensional slices of the cube (which, if the field of view is held fixed, limits the spatial resolution). In the past decade, however, there have been a number of ingenious designs that allow independent control of three of these quantities simultaneously [41][42][43], largely through the introduction of various multiplex measurement techniques.…”

Section: Compressive Spectral Imagingmentioning

confidence: 99%

Compressive Optical Imaging Systems -- Theory, Devices and Implementation

Baraniuk¹,

Kelly²,

Brady³

et al. 2009

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Standard Form 298 Prescribed by ANSI-Std Z39-18 REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Rice University 6100 Main St. MS 16 Houston TX 77005 PERFORMING ORGANIZATION REPORT NUMBER SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)Dennis Healy DARPA, Microsystems Technology Office 3701 N. Fairfax Dr. Arlington VA 22203-1714 SPONSOR/MONITOR'S ACRONYM(S)DARPA MTO/SPAWAR SPONSORING/MONITORING AGENCY REPORT NUMBER DISTRIBUTION AVAILABILITY STATEMENTApproved for public release; distribution is unlimited SUPPLEMENTARY NOTES ABSTRACTIn this project we expanded the theory of compressive sensing and explored the connection of CS to advanced optical imaging systems. In regard to theory we incorporated a Hidden Markov Tree signal model into existing CS theory and produced an algorithm for fast signal recover.We physically implemented CS on a single-pixel imaging testbed and analyzed its performance against existing multiplexing schemes. The insight of CS theory and experience in CS implementation culminated with the creation of our single-shot hyperspectral imager. SUBJECT TERMSanalog-to-information conversion, compressive sampling, sparsity, random filter, random sampling

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“…Other groups have focused on simpler schemes that are more robust and inexpensive, most notably several direct-view designs that maximize the light gathering efficiency of the systems [11][12][13]. These systems do away with the spectrometer slit altogether and simply view the source through a rotating dispersive element.…”

Section: Imaging System Design 21 Design Motivationmentioning

confidence: 99%

Multiscale reconstruction for computational spectral imaging

2007

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In this work we develop a spectral imaging system and associated reconstruction methods that have been designed to exploit the theory of compressive sensing. Recent work in this emerging field indicates that when the signal of interest is very sparse (i.e. zero-valued at most locations) or highly compressible in some basis, relatively few incoherent observations are necessary to reconstruct the most significant non-zero signal components. Conventionally, spectral imaging systems measure complete data cubes and are subject to performance limiting tradeoffs between spectral and spatial resolution. We achieve single-shot full 3D data cube estimates by using compressed sensing reconstruction methods to process observations collected using an innovative, real-time, dual-disperser spectral imager. The physical system contains a transmissive coding element located between a pair of matched dispersers, so that each pixel measurement is the coded projection of the spectrum in the corresponding spatial location in the spectral data cube. Using a novel multiscale representation of the spectral image data cube, we are able to accurately reconstruct 256 × 256 × 15 spectral image cubes using just 256 × 256 measurements.Keywords: wavelets, compressed sensing, hyperspectral imaging COMPRESSIVE SPECTRAL IMAGINGSpectral imaging is a highly effective tool in a variety of scientific and engineering contexts because of the information it implicitly encodes about the nature of the materials being imaged. While traditional digital imaging techniques produce images with scalar values associated with each spatial pixel location, in spectral images these scalar values are replaced with a vector containing the spectral information from that spatial location. The resulting data is therefore three-dimensional (two spatial dimensions and one spectral dimension). Spectral information can be vital for tasks such as monitoring the health of a forest ecosystem [1,2], increasing our understanding of solar physics [3], or examining tissue and organisms used to study cellular dynamics [4, 5].Despite its potential, however, many modern spectral imagers face a limiting tradeoff between spatial and spectral resolution, with the total number of voxels measured constrained by the size of the detector array. This constraint limits the utility and cost-effectiveness of spectral imaging for many applications. Furthermore, straightforward application of traditional spectroscopic techniques to spectral imaging proves problematic. The simplest spectral imager forms combine either a pushbroom (linear scanning) or tomographic (rotational scanning) front-end with a traditional slit-based dispersive spectrometer. Unfortunately, in many of the most interesting applications (most notably biophotonics) the sources are often both spatially-incoherent and weak. Spatially-incoherent sources present a significant challenge to slit-based dispersive spectrometers, and extremely poor photon collection efficiency is the result. When the source is also weak, the abso...

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High-throughput hyperspectral infrared camera

Cited by 82 publications

References 12 publications

Low-bias, high-temperature operation of an InAs–InGaAs quantum-dot infrared photodetector with peak-detection wavelength of 11.7μm

Low-bias, high-temperature operation of an InAs–InGaAs quantum-dot infrared photodetector with peak-detection wavelength of 11.7μm

Compressive Optical Imaging Systems -- Theory, Devices and Implementation

Multiscale reconstruction for computational spectral imaging

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