Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network

In this paper, a phase unwrapping method based on Residual Auto Encoder Network is proposed. Phase unwrapping is regarded as a multiple classification problem and it will be solved by the trained network model. Through training and validation stages, optimal network model can be served as a predictor to predict wrap count distribution map of wrapped phase. Then merge wrapped phase and count together to complete unwrapping. Software simulation and hardware acquisition are the sources for the generation of training dataset. To further improve the accuracy of unwrapping, an image analysis based optimization method is designed that can remove misclassification and noise points in initial result. In addition, phase data stitching by Iterative Closest Point (ICP) is adopted to realize dynamic resolution and enhance the flexibility of method. Point diffraction interferometer (PDI) and multi-step phase extraction technique are introduced, which is the foundation of proposed method. It can be concluded from the experiments that the proposed method is superior to state-of-art ones in terms of unwrapping performance, time efficiency, anti-noise ability and flexibility.