Depth_ ASPP_ UNet++: A novel 2D ESPI phase unwrapping based on convolutional neural network

Abstract Phase unwrapping is a key step in optical metrology and physical optics to obtain accurate 
phase distributions. In practice, phase images obtained from electronic speckle pattern 
interferometry (ESPI) exhibit diverse and complex morphology, with significant shape 
variations and non-uniform densities among different individuals. This takes challenges for 
accurately extracting phase information and unwrapping the phase. With the progress of deep 
learning technology in optical image processing, real-time performance and accuracy have 
become concerned issues. In this paper, an ESPI phase unwrapping method based on 
convolutional neural network (CNN) UNet++ is proposed. The proposed network combines 
the depthwise separable convolution (DSC), atrous spatial pyramid pooling (ASPP), defined 
as Depth_ ASPP_ UNet++. In this model, the use of DSC improves network computational 
efficiency and provides better feature representation capability. In addition, ASPP is 
introduced to pay more attention to the phase information of the phase image, and then obtain 
better phase unwrapping results. The experimental results show that our proposed method can 
obtain excellent results, especially with various of variable density, different noise levels, and 
different speckle sizes.