Eliminating Temporal Illumination Variations in Whisk-broom Hyperspectral Imaging

We propose a method for eliminating the temporal illumination variations in whisk-broom (point-scan) hyperspectral imaging. Whisk-broom scanning is useful for acquiring a spatial measurement using a pixel-based hyperspectral sensor. However, when it is applied to outdoor cultural heritages, temporal...

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Bibliographic Details
Published in:International journal of computer vision Vol. 130; no. 5; pp. 1310 - 1324
Main Authors: Funatomi, Takuya, Ogawa, Takehiro, Tanaka, Kenichiro, Kubo, Hiroyuki, Caron, Guillaume, Mouaddib, El Mustapha, Matsushita, Yasuyuki, Mukaigawa, Yasuhiro
Format: Journal Article
Language:English
Published: New York Springer US 01.05.2022
Springer
Springer Nature B.V
Springer Verlag
Subjects:
Artificial Intelligence
Cathedrals
Computer Imaging
Computer Science
Computer Vision and Pattern Recognition
Hyperspectral imaging
Illumination
Image Processing and Computer Vision
Infrared radiation
Pattern Recognition
Pattern Recognition and Graphics
Raster
Raster scanning
Scanning devices
Sensors
Special Issue on Computer Vision for Cultural Heritage
Vision
Low-rank approximation
Computational photography
Varying illumination
Hyperspectral imaging
ISSN:0920-5691, 1573-1405
Online Access:Get full text
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Summary:We propose a method for eliminating the temporal illumination variations in whisk-broom (point-scan) hyperspectral imaging. Whisk-broom scanning is useful for acquiring a spatial measurement using a pixel-based hyperspectral sensor. However, when it is applied to outdoor cultural heritages, temporal illumination variations become an issue due to the lengthy measurement time. As a result, the incoming illumination spectra vary across the measured image locations because different locations are measured at different times. To overcome this problem, in addition to the standard raster scan, we propose an additional perpendicular scan that traverses the raster scan. We show that this additional scan allows us to infer the illumination variations over the raster scan. Furthermore, the sparse structure in the illumination spectrum is exploited to robustly eliminate these variations. We quantitatively show that a hyperspectral image captured under sunlight is indeed affected by temporal illumination variations, that a Naïve mitigation method suffers from severe artifacts, and that the proposed method can robustly eliminate the illumination variations. Finally, we demonstrate the usefulness of the proposed method by capturing historic stained-glass windows of a French cathedral.
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ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-022-01587-8