论文标题
用于计算光学形式测量的深度神经网络
Deep Neural Networks for Computational Optical Form Measurements
论文作者
论文摘要
深度神经网络已成功应用于许多不同领域，例如计算成像，医疗保健，信号处理或自主驾驶。在原理证明的研究中，我们证明了计算光学形式测量也可以从深度学习中受益。探索了一种数据驱动的机器学习方法，以在光学表面的准确测量中解决一个反问题。使用具有已知地面真相的虚拟测量来开发和测试该方法。
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.
