Binarization of ESPI fringe patterns based on an M-net convolutional neural network.

The fringe skeleton method is the most straightforward method to estimate phase terms in electronic speckle pattern interferometry (ESPI). It usually needs to binarize the fringe patterns. However, the massive inherent speckle noise and intensity inhomogeneity in ESPI fringe patterns make it difficult to binarize the ESPI fringe patterns. In this paper, we propose a binarization method for ESPI fringe patterns based on a modified M-net convolutional neural network. Our method regards the binarization of fringe patterns as a segmentation problem. The M-net is an excellent network for segmentation and has proven to be a useful tool for skeleton extraction in our previous work. Here we further modify the structure of the previous network a bit to suit our task. We train the network by pairs of ESPI fringe patterns and corresponding binary images. After training, we test our method on 20 computer-simulated and three groups of experimentally obtained ESPI fringe patterns. The results show that even for fringe patterns with high noise and intensity inhomogeneity, our method can obtain good binarization results without image preprocessing. We also compare the modified M-net with a classic segmentation network, the U-net, and a residual encoder-decoder network (RED-net). The RED-net was used for binarization of document images. The experimental results prove the effectiveness of our method.