We propose a deep-learning based deflectometric method for freeform surface measurement, in which a deep neural network is devised for freeform surface reconstruction. Full-scale skip connections are adopted in the network architecture to extract and incorporate multi-scale feature maps from different layers, enabling the accuracy and robustness of the testing system to be greatly enhanced. The feasibility of the proposed method is numerically and experimentally validated, and its excellent performance in terms of accuracy and robustness is also demonstrated. The proposed method provides a feasible way to achieve the general measurement of freeform surfaces while minimizing the measurement errors due to noise and system geometry calibration.
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics