Deep neural networks for computational optical form measurements

Hoffmann, Lara; Elster, Clemens

Deep neural networks have been successfully applied in many different fields like computational imaging, healthcare, signal processing, or autonomous driving. In a proof-of-principle study, we demonstrate that computational optical form measurement can also benefit from deep learning. A data-driven machine-learning approach is explored to solve an inverse problem in the accurate measurement of optical surfaces. The approach is developed and tested using virtual measurements with a known ground truth.

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Hoffmann, Lara / Elster, Clemens: Deep neural networks for computational optical form measurements. 2020. Copernicus Publications.

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Rechteinhaber: Lara Hoffmann

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