Development of four-dimensional imaging spectrometers (4D-IS)

Gat, Nahum; Scriven, Gordon; Garman, John; Li, Ming De; Zhang, Jingyi

doi:10.1117/12.678082

Cited by 40 publications

(23 citation statements)

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“…Snapshot approaches can be implemented by multiplexing a high-dimensional signal onto a 2D sensor, thereby sacrificing image spatial resolution [Manakov et al 2013]. Four-dimensional imaging spectrometer (4DIS) [Gat et al 2006] uses a coherent 2D to 1D fiber array, and the datacube has been collimated, dispersed, and re-imaged onto the sensor, so that each fiber's spectrum can be extracted and re-arranged into a datacube. Image mapping spectrometer (IMS) [Gao et al 2010] establishes the mapping correspondence between the voxels in the datacube and the pixels on a large format sensor by using a custom mapping mirror and a prism.…”

Section: Related Workmentioning

confidence: 99%

Spatial-spectral encoded compressive hyperspectral imaging

et al. 2014

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Figure 1: 3D hyperspectral (HS) images reconstructed from a single spatial-spectral encoded 2D projection for an outdoor scene under sunlight environment. Left-most: The sensor image captured by our camera prototype. Second-by-left: Recovered high-spatial resolution 3D HS images with color indicating 31 spectral bands from 420nm to 720nm. Third and fourth: Closeup of recovered 520nm and 650nm spectral bands images. Right-most: The synthetic RGB image from the reconstructed HS images. AbstractThis paper proposes a novel compressive hyperspectral (HS) imaging approach that allows for high-resolution HS images to be captured in a single image. The proposed architecture comprises three key components: spatial-spectral encoded optical camera design, over-complete HS dictionary learning and sparse-constraint computational reconstruction. Our spatial-spectral encoded sampling scheme provides a higher degree of randomness in the measured projections than previous compressive HS imaging approaches; and a robust nonlinear sparse reconstruction method is employed to recover the HS images from the coded projection with higher performance. To exploit the sparsity constraint on the nature HS images for computational reconstruction, an over-complete HS dictionary is learned to represent the HS images in a sparser way than previous representations. We validate the proposed approach on both synthetic and real captured data, and show successful recovery of HS images for both indoor and outdoor scenes. In addition, we demonstrate other applications for the over-complete HS dictionary and sparse coding techniques, including 3D HS images compression and denoising.

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Section: Related Workmentioning

confidence: 99%

Spatial-spectral encoded compressive hyperspectral imaging

et al. 2014

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“…82 This was followed by systems developed by other research groups. [83][84][85][86][87] 4.3 Integral Field Spectroscopy with Lenslet Arrays (IFS-L, 1960)…”

Section: Integral Field Spectrometry With Coherent Fiber Bundles (Ifsmentioning

confidence: 99%

Review of snapshot spectral imaging technologies

2013

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Within the field of spectral imaging, the vast majority of instruments used are scanning devices. Recently, several snapshot spectral imaging systems have become commercially available, providing new functionality for users and opening up the field to a wide array of new applications. A comprehensive survey of the available snapshot technologies is provided, and an attempt has been made to show how the new capabilities of snapshot approaches can be fully utilized. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…17–23 For applications such as biomedical imaging 25 and remote sensing, tunability and compactness are two major properties of HSI systems. Tunability allows us to adjust both spatial and spectral resolutions to meet specific application requirements.…”

Section: Introductionmentioning

confidence: 99%

“…20,21 In most of these fiber-based snapshot spectrometer designs, the output end of the fiber bundle is reformatted into a single fiber column that acts as the input slit to a dispersive hyperspectral imaging system. 17–23 Thus, the maximum number of fibers in the fiber column is limited by the detector array length, resulting in a fundamental limitation to the spatial sampling density which can be achieved. To date, the largest reported spatial sampling in fiber-based snapshot spectrometry was 44 × 40 = 1760 by Kriesel et al, 22 where the authors split and directed elements of a single fiber bundle to four separate spectrometers and subsequently recombined the datacube in postprocessing.…”

Section: Introductionmentioning

confidence: 99%

High spatial sampling light-guide snapshot spectrometer

2017

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A prototype fiber-based imaging spectrometer was developed to provide snapshot hyperspectral imaging tuned for biomedical applications. The system is designed for imaging in the visible spectral range from 400 to 700 nm for compatibility with molecular imaging applications as well as satellite and remote sensing. An 81 × 96 pixel spatial sampling density is achieved by using a custom-made fiber-optic bundle. The design considerations and fabrication aspects of the fiber bundle and imaging spectrometer are described in detail. Through the custom fiber bundle, the image of a scene of interest is collected and divided into discrete spatial groups, with spaces generated in between groups for spectral dispersion. This reorganized image is scaled down by an image taper for compatibility with following optical elements, dispersed by a prism, and is finally acquired by a CCD camera. To obtain an (x, y, λ) datacube from the snapshot measurement, a spectral calibration algorithm is executed for reconstruction of the spatial–spectral signatures of the observed scene. System characterization of throughput, resolution, and crosstalk was performed. Preliminary results illustrating changes in oxygen-saturation in an occluded human finger are presented to demonstrate the system’s capabilities.

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Development of four-dimensional imaging spectrometers (4D-IS)

Cited by 40 publications

References 0 publications

Spatial-spectral encoded compressive hyperspectral imaging

Spatial-spectral encoded compressive hyperspectral imaging

Review of snapshot spectral imaging technologies

High spatial sampling light-guide snapshot spectrometer

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