The Experts below are selected from a list of 24132 Experts worldwide ranked by ideXlab platform
Gonzalo R Arce - One of the best experts on this subject based on the ideXlab platform.
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synthetic coded apertures in compressive spectral imaging
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Laura Galvis, Henry Arguello, Gonzalo R ArceAbstract:Compressive spectral imagers have gained popularity recently due to their ability to sense a three-dimensional (3D) Data Cube with just a few two dimensional (2D) coded aperture projection snapshots. The coded apertures are realized by digital micromirror devices (DMD) which often do not match the pitch resolution of the focal plane array (FPA). This paper introduces the forward model and associated reconstruction algorithm for such mismatched spectral imagers, without the loss of spectral and spatial resolution. Simulations show the improvements in the reconstructions achieved with the proposed approach yielding up to 12 dB gain in PSNR with respect to traditional.
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compressive coded aperture spectral imaging an introduction
IEEE Signal Processing Magazine, 2014Co-Authors: Gonzalo R Arce, Henry Arguello, David J Brady, Lawrence Carin, David S KittleAbstract:Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral Data Cube. Push broom spectral imaging sensors, for instance, capture a spectral Cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire Data Cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.
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restricted isometry property in coded aperture compressive spectral imaging
IEEE Signal Processing Workshop on Statistical Signal Processing, 2012Co-Authors: Henry Arguello, Gonzalo R ArceAbstract:Coded Aperture Snapshot Spectral Imaging Systems (CASSI) capture the spectral information of a scene using a set of coded focal plane array measurements. Compressed sensing reconstruction algorithms are used to reconstruct the underlying spectral 3D Data Cube. The coded measurements in CASSI use structured sensing matrices. This article describes the Restricted Isometry Property (RIP) for the projection matrices used in CASSI. In turn, the RIP provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction. It also provides the optimal transmittance parameters for the set of code apertures used in the acquisition process.
Erik Rosolowsky - One of the best experts on this subject based on the ideXlab platform.
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structural analysis of molecular clouds dendrograms
arXiv: Astrophysics, 2008Co-Authors: Erik Rosolowsky, Jaime E Pineda, Jens Kauffmann, Alyssa A GoodmanAbstract:We demonstrate the utility of dendrograms at representing the essential features of the hierarchical structure of the isosurfaces for molecular line Data Cubes. The dendrogram of a Data Cube is an abstraction of the changing topology of the isosurfaces as a function of contour level. The ability to track hierarchical structure over a range of scales makes this analysis philosophically different from local segmentation algorithms like CLUMPFIND. Points in the dendrogram structure correspond to specific volumes in Data Cubes defined by their bounding isosurfaces. We further refine the technique by measuring the properties associated with each isosurface in the analysis allowing for a multiscale calculation of molecular gas properties. Using COMPLETE 13CO(1-0) Data from the L1448 region in Perseus and mock observations of a simulated Data Cube, we identify regions that have a significant contribution by self-gravity to their energetics on a range of scales. We find evidence for self-gravitation on all spatial scales in L1448 though not in all regions. In the simulated observations, nearly all of the emission is found in objects that would be self-gravitating if gravity were included in the simulation. We reconstruct the size-line width relationship within the Data Cube using the dendrogram-derived properties and find it follows the standard relation: s_v ~ R^0.58. Finally, we show that constructing the dendrogram of CO J=1-0 emission from the Orion-Monoceros region allows for the identification of giant molecular clouds in a blended molecular line Data set using only a physically motivated definition (self-gravitating clouds with masses 5x10^4 Msun.
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the spectral correlation function a new tool for analyzing spectral line maps
The Astrophysical Journal, 1999Co-Authors: Erik Rosolowsky, Alyssa A Goodman, David J Wilner, Jonathan P WilliamsAbstract:The "spectral correlation function" analysis we introduce in this paper is a new tool for analyzing spectral line Data Cubes. Our initial tests, carried out on a suite of observed and simulated Data Cubes, indicate that the spectral correlation function (SCF) is likely to be a more discriminating statistic than other statistical methods normally applied. The SCF is a measure of similarity between neighboring spectra in the Data Cube. When the SCF is used to compare a Data Cube consisting of spectral line observations of the interstellar medium (ISM) with a Data Cube derived from MHD simulations of molecular clouds, it can find differences that are not found by other analyses. The initial results presented here suggest that the inclusion of self-gravity in numerical simulations is critical for reproducing the correlation behavior of spectra in star-forming molecular clouds.
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the spectral correlation function a new tool for analyzing spectral line maps
arXiv: Astrophysics, 1999Co-Authors: Erik Rosolowsky, Alyssa A Goodman, David J Wilner, Jonathan P WilliamsAbstract:The "spectral correlation function" analysis we introduce in this paper is a new tool for analyzing spectral-line Data Cubes. Our initial tests, carried out on a suite of observed and simulated Data Cubes, indicate that the spectral correlation function [SCF] is likely to be a more discriminating statistic than other statistical methods normally applied. The SCF is a measure of similarity between neighboring spectra in the Data Cube. When the SCF is used to compare a Data Cube consisting of spectral-line observations of the ISM with a Data Cube derived from MHD simulations of molecular clouds, it can find differences that are not found by other analyses. The initial results presented here suggest that the inclusion of self-gravity in numerical simulations is critical for reproducing the correlation behavior of spectra in star-forming molecular clouds.
David S Kittle - One of the best experts on this subject based on the ideXlab platform.
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compressive coded aperture spectral imaging an introduction
IEEE Signal Processing Magazine, 2014Co-Authors: Gonzalo R Arce, Henry Arguello, David J Brady, Lawrence Carin, David S KittleAbstract:Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral Data Cube. Push broom spectral imaging sensors, for instance, capture a spectral Cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire Data Cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.
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multiframe image estimation for coded aperture snapshot spectral imagers
Applied Optics, 2010Co-Authors: David S Kittle, Ashwin A Wagadarikar, Kerkil Choi, David J BradyAbstract:A coded aperture snapshot spectral imager (CASSI) estimates the three-dimensional spatiospectral Data Cube from a snapshot two-dimensional coded projection, assuming that the scene is spatially and spectrally sparse. For less spectrally sparse scenes, we show that the use of multiple nondegenerate snapshots can make Data Cube recovery less ill-posed, yielding improved spatial and spectral reconstruction fidelity. Additionally, Data acquisition can be easily scaled to meet the time/resolution requirements of the scene with little modification or extension of the original CASSI hardware. A multiframe reconstruction of a 640×480×53 voxel DataCube with 450–650nm white-light illumination of a scene reveals substantial improvement in the reconstruction fidelity, with limited increase in acquisition and reconstruction time.
Henry Arguello - One of the best experts on this subject based on the ideXlab platform.
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synthetic coded apertures in compressive spectral imaging
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Laura Galvis, Henry Arguello, Gonzalo R ArceAbstract:Compressive spectral imagers have gained popularity recently due to their ability to sense a three-dimensional (3D) Data Cube with just a few two dimensional (2D) coded aperture projection snapshots. The coded apertures are realized by digital micromirror devices (DMD) which often do not match the pitch resolution of the focal plane array (FPA). This paper introduces the forward model and associated reconstruction algorithm for such mismatched spectral imagers, without the loss of spectral and spatial resolution. Simulations show the improvements in the reconstructions achieved with the proposed approach yielding up to 12 dB gain in PSNR with respect to traditional.
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compressive coded aperture spectral imaging an introduction
IEEE Signal Processing Magazine, 2014Co-Authors: Gonzalo R Arce, Henry Arguello, David J Brady, Lawrence Carin, David S KittleAbstract:Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral Data Cube. Push broom spectral imaging sensors, for instance, capture a spectral Cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire Data Cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.
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restricted isometry property in coded aperture compressive spectral imaging
IEEE Signal Processing Workshop on Statistical Signal Processing, 2012Co-Authors: Henry Arguello, Gonzalo R ArceAbstract:Coded Aperture Snapshot Spectral Imaging Systems (CASSI) capture the spectral information of a scene using a set of coded focal plane array measurements. Compressed sensing reconstruction algorithms are used to reconstruct the underlying spectral 3D Data Cube. The coded measurements in CASSI use structured sensing matrices. This article describes the Restricted Isometry Property (RIP) for the projection matrices used in CASSI. In turn, the RIP provides guidelines for the minimum number of FPA measurement shots needed for image reconstruction. It also provides the optimal transmittance parameters for the set of code apertures used in the acquisition process.
David J Brady - One of the best experts on this subject based on the ideXlab platform.
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compressive coded aperture spectral imaging an introduction
IEEE Signal Processing Magazine, 2014Co-Authors: Gonzalo R Arce, Henry Arguello, David J Brady, Lawrence Carin, David S KittleAbstract:Imaging spectroscopy involves the sensing of a large amount of spatial information across a multitude of wavelengths. Conventional approaches to hyperspectral sensing scan adjacent zones of the underlying spectral scene and merge the results to construct a spectral Data Cube. Push broom spectral imaging sensors, for instance, capture a spectral Cube with one focal plane array (FPA) measurement per spatial line of the scene [1], [2]. Spectrometers based on optical bandpass filters sequentially scan the scene by tuning the bandpass filters in steps. The disadvantage of these techniques is that they require scanning a number of zones linearly in proportion to the desired spatial and spectral resolution. This article surveys compressive coded aperture spectral imagers, also known as coded aperture snapshot spectral imagers (CASSI) [1], [3], [4], which naturally embody the principles of compressive sensing (CS) [5], [6]. The remarkable advantage of CASSI is that the entire Data Cube is sensed with just a few FPA measurements and, in some cases, with as little as a single FPA shot.
-
multiframe image estimation for coded aperture snapshot spectral imagers
Applied Optics, 2010Co-Authors: David S Kittle, Ashwin A Wagadarikar, Kerkil Choi, David J BradyAbstract:A coded aperture snapshot spectral imager (CASSI) estimates the three-dimensional spatiospectral Data Cube from a snapshot two-dimensional coded projection, assuming that the scene is spatially and spectrally sparse. For less spectrally sparse scenes, we show that the use of multiple nondegenerate snapshots can make Data Cube recovery less ill-posed, yielding improved spatial and spectral reconstruction fidelity. Additionally, Data acquisition can be easily scaled to meet the time/resolution requirements of the scene with little modification or extension of the original CASSI hardware. A multiframe reconstruction of a 640×480×53 voxel DataCube with 450–650nm white-light illumination of a scene reveals substantial improvement in the reconstruction fidelity, with limited increase in acquisition and reconstruction time.
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single disperser design for coded aperture snapshot spectral imaging
Applied Optics, 2008Co-Authors: Ashwin A Wagadarikar, Renu John, Rebecca Willett, David J BradyAbstract:We present a single disperser spectral imager that exploits recent theoretical work in the area of compressed sensing to achieve snapshot spectral imaging. An experimental prototype is used to capture the spatiospectral information of a scene that consists of two balls illuminated by different light sources. An iterative algorithm is used to reconstruct the Data Cube. The average spectral resolution is 3.6 nm per spectral channel. The accuracy of the instrument is demonstrated by comparison of the spectra acquired with the proposed system with the spectra acquired by a nonimaging reference spectrometer.
