Abstract

By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.
Original languageEnglish
Pages (from-to)5514-5530
Number of pages17
JournalOptics Express
Volume26
Issue number5
DOIs
Publication statusPublished - 23 Feb 2018

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Photons
Color
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Keywords

  • Image reconstruction techniques
  • Photon counting
  • Color
  • Lidar

Cite this

@article{5764ed2959ea480199c824cf667e927f,
title = "Spectral classification of sparse photon depth images",
abstract = "By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.",
keywords = "Image reconstruction techniques, Photon counting, Color, Lidar",
author = "Yoann Altmann and Aurora Maccarone and Aongus McCarthy and Stephen McLaughlin and Buller, {Gerald Stuart}",
year = "2018",
month = "2",
day = "23",
doi = "10.1364/OE.26.005514",
language = "English",
volume = "26",
pages = "5514--5530",
journal = "Optics Express",
issn = "1094-4087",
publisher = "Optical Society of America",
number = "5",

}

Spectral classification of sparse photon depth images. / Altmann, Yoann; Maccarone, Aurora; McCarthy, Aongus; McLaughlin, Stephen; Buller, Gerald Stuart.

In: Optics Express, Vol. 26, No. 5, 23.02.2018, p. 5514-5530.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Spectral classification of sparse photon depth images

AU - Altmann, Yoann

AU - Maccarone, Aurora

AU - McCarthy, Aongus

AU - McLaughlin, Stephen

AU - Buller, Gerald Stuart

PY - 2018/2/23

Y1 - 2018/2/23

N2 - By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.

AB - By illumination of target scenes using a set of different wavelengths, we demonstrate color classification of scenes, as well as depth estimation, in photon-starved images. The spectral signatures are classified with a new advanced statistical image processing method from measurements of the same scene, in this case using combinations of 33, 16, 8 or 4 different wavelengths in the range 500 – 820 nm. This approach makes it possible to perform color classification and depth estimation on images containing as few as one photon per pixel, on average. Compared to single wavelength imaging, this approach improves target discrimination by extracting more spectral information, which, in turn, improves the depth estimation since this approach is robust to changes in target reflectivity. We demonstrate color classification and depth profiling of complex targets at average signal levels as low as 1.0 photons per pixel from as few as 4 different wavelength measurements.

KW - Image reconstruction techniques

KW - Photon counting

KW - Color

KW - Lidar

U2 - 10.1364/OE.26.005514

DO - 10.1364/OE.26.005514

M3 - Article

VL - 26

SP - 5514

EP - 5530

JO - Optics Express

JF - Optics Express

SN - 1094-4087

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ER -