Fully automated extraction of the fringe skeletons in dynamic electronic speckle pattern interferometry using a U-Net convolutional neural network

Abstract

With the development of artificial intelligence technology, intelligent fringe processing is a goal of relevant researchers in optical interferometry. We propose an intelligent method to achieve fully automated extraction of the fringe skeletons in electronic speckle pattern interferometry (ESPI) based on U-Net convolutional neural network. In the proposed method, the network is first trained by the samples that consist of the noisy ESPI fringe patterns and the corresponding skeleton images. After training, the other multiframe ESPI fringe patterns are fed to the trained network simultaneously; the corresponding skeleton images can be obtained in batches. Using our method, it is not necessary to process fringe patterns frame by frame. Our method is especially suitable for multiframe fringe patterns processing. We apply the intelligent method to one computer-simulated and one real-dynamic ESPI measurement, respectively. For the simulated measurement, it takes just 40 s to obtain the skeleton images of 20 noisy ESPI fringe patterns using our method. Even for low-quality experimental obtained ESPI fringe patterns, our method can also give desired results.