Antiderivative of gradient data by spline model integration

Antiderivative of gradient data by spline model integration

Numerous optical techniques describe the local slope of the functions at their discrete positions but do not report the actual functions. However, many applications require the description of the functions, which must be retrieved from the gradients by an integration process. This study shows a spli...

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Veröffentlicht in:Journal of the Optical Society of America. A, Optics, image science, and vision Optics, image science, and vision, 2021-08, Vol.38 (8), p.1187-1193
Hauptverfasser: Badar, Irfan, Yang, Liangxin, Hellmann, Christian, Wyrowski, Frank
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container_issue 8
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container_title Journal of the Optical Society of America. A, Optics, image science, and vision
container_volume 38
creator Badar, Irfan
Yang, Liangxin
Hellmann, Christian
Wyrowski, Frank
description Numerous optical techniques describe the local slope of the functions at their discrete positions but do not report the actual functions. However, many applications require the description of the functions, which must be retrieved from the gradients by an integration process. This study shows a spline model function-based integration technique that can construct original functions from irregularly measured gradient data over general shape domains with high accuracy and speed.
doi_str_mv 10.1364/JOSAA.430638
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title Antiderivative of gradient data by spline model integration
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