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Volume: 27 | Article ID: art00054
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Spectral image recovery from Spectral Filter Array cameras using LMMSE
Prakhar Amba
David Alleysson
  DOI :  10.2352/issn.2169-2629.2019.27.54  Published Online :  October 2019
Abstract

A hyperspectral camera can record a cube of data with both spatial 2D and spectral 1D dimensions. Spectral Filter Arrays (SFAs) overlaid on a single sensor allows a snapshot version of a hyperspectral camera. But acquired image is subsampled both spatially and spectrally, and a recovery method should be applied. In this paper we present a linear model of spectral and spatial recovery based on Linear Minimum Mean Square Error (LMMSE) approach. The method learns a stable linear solution for which redundancy is controlled using spatial neighborhood. We evaluate results in simulation using gaussian shaped filter's sensitivities on SFA mosaics of upto 9 filters with sensitivities both in visible and Near-Infrared (NIR) wavelength. We show by experiment that by using big neighborhood sizes in our model we can accurately recover the spectra from the RAW images taken by such a camera. We also present results on recovered spectra of Macbeth color chart from a Bayer SFA having 3 filters.

Journal Title : Color and Imaging Conference
Publisher Name : Society for Imaging Science and Technology
Subject Areas :
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 DOI 10.2352/issn.2169-2629.2019.27.54
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Prakhar Amba, David Alleysson, "Spectral image recovery from Spectral Filter Array cameras using LMMSE"  in Proc. IS&T 27th Color and Imaging Conf.,  2019,  pp 300 - 303,  https://doi.org/10.2352/issn.2169-2629.2019.27.54

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Copyright © Society for Imaging Science and Technology 2019
72010350
Color and Imaging Conference
color imaging conf
2166-9635
Society for Imaging Science and Technology
10.2352/issn.2169-2629.2019.27.54
2166-9635(20191021)2019:1L.300;1-
s54.phd
/ist/cic/2019/00002019/00000001/art00054
Articles
Spectral image recovery from Spectral Filter Array cameras using LMMSE
AmbaPrakhar
AlleyssonDavid
21102019
2019
1
300
303
2019
A hyperspectral camera can record a cube of data with both spatial 2D and spectral 1D dimensions. Spectral Filter Arrays (SFAs) overlaid on a single sensor allows a snapshot version of a hyperspectral camera. But acquired image is subsampled both spatially and spectrally, and a recovery method should be applied. In this paper we present a linear model of spectral and spatial recovery based on Linear Minimum Mean Square Error (LMMSE) approach. The method learns a stable linear solution for which redundancy is controlled using spatial neighborhood. We evaluate results in simulation using gaussian shaped filter's sensitivities on SFA mosaics of upto 9 filters with sensitivities both in visible and Near-Infrared (NIR) wavelength. We show by experiment that by using big neighborhood sizes in our model we can accurately recover the spectra from the RAW images taken by such a camera. We also present results on recovered spectra of Macbeth color chart from a Bayer SFA having 3 filters.