ABSTRACT
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.
ABSTRACT
Deflectometry, with its noticeable advantages such as simple structure, large dynamic range, and high accuracy comparable to interferometry, has been one of the powerful metrological techniques for optical surfaces in recent years. In the "null" deflectometric transmitted wavefront testing of refractive optics, ray tracing of the test system model is required, in which both the miscalibration of system geometrical parameters and optical tolerances on tested optics could introduce significant geometrical aberrations in the testing results. In this paper, the geometrical aberration introduced by a system modeling error in the transmitted wavefront testing is discussed. Besides, a calibration method based on polynomial optimization of geometrical aberration is presented for the geometrical aberration calibration. Both simulation and experiment have been performed to validate the feasibility of the proposed calibration method. The proposed method can calibrate the optical tolerances on tested optics effectively, and it is feasible even with a large geometric error, providing a viable way to address the uncertainty in system modeling in transmitted wavefront testing of freeform refractive optics with large dynamic range.
