Deep neural networks for computational optical form measurements
Lara Hoffmann, Clemens Elster · Journal of sensors and sensor systems · 2020
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.