Deep Neural Networks for Computational Optical Form Measurements
This work addresses the challenge of precise optical surface measurement, which is important for fields like optics and manufacturing, but it is incremental as it applies existing deep learning methods to a new domain.
The authors tackled the problem of accurately measuring optical surfaces by applying deep neural networks to solve an inverse problem in computational optical form measurement, demonstrating this in a proof-of-principle study using virtual measurements with known ground truth.
Deep neural networks have been successfully applied in many different fields like computational imaging, medical 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 known ground truth.