Channel transformer U-Net: an automatic and effective skeleton extraction network for electronic speckle pattern interferometry

Abstract

The fringe skeleton extraction method may be the most straightforward method for electronic speckle pattern interferometry (ESPI) phase extraction. Due to ESPI fringe patterns having the characteristics of high noise, low contrast, and different fringe shapes, it is very difficult to extract skeletons from ESPI fringe patterns with high accuracy. To deal with this problem, we propose a skeleton extraction method based on deep learning, called channel transformer U-Net, for directly extracting skeletons from noisy ESPI fringe patterns. In the proposed method, the advanced channel-wise cross fusion transformer module is integrated into the design of deep U-Net architecture, and a loss function by combining binary cross entropy loss and poly focal loss is proposed. In addition, a marking algorithm is proposed for phase extraction, which can realize automatic identification of a skeleton line. The effectiveness of the above proposed algorithms has been verified by computer-simulated and real-dynamic ESPI measurements. The experimental results demonstrate that the proposed channel transformer U-Net can obtain accurate, complete, and smooth skeletons in all cases. The accuracy of the skeleton extraction obtained by our proposed network can reach 0.9878, and the correlation coefficient value can reach 0.9905. The skeleton line automatic marking algorithm has strong universality.