Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networks Article Swipe
Paul Myland
,
Sebastian Babilon
,
Tim Hegemann
,
Stefan Klir
,
Tran Quoc Khanh
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/oe.489449
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1364/oe.489449
Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δ u ′ v ′ of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1364/oe.489449
- OA Status
- gold
- Cited By
- 12
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379232022
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379232022Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1364/oe.489449Digital Object Identifier
- Title
-
Reconstruction of spectral irradiance in a real application with a multi-channel spectral sensor using convolutional neural networksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-06-02Full publication date if available
- Authors
-
Paul Myland, Sebastian Babilon, Tim Hegemann, Stefan Klir, Tran Quoc KhanhList of authors in order
- Landing page
-
https://doi.org/10.1364/oe.489449Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1364/oe.489449Direct OA link when available
- Concepts
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Illuminance, Irradiance, Computer science, Daylight, Spectral power distribution, Multispectral image, Color rendering index, Spectral shape analysis, Spectroradiometer, Color temperature, Optics, Remote sensing, Artificial intelligence, Light-emitting diode, Physics, Spectral line, Reflectivity, Geology, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 7, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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