PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI

Abstract

In this paper, we propose a deep nonlinear CNN model, named as PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI. Our PU-M-Net consists of four pathways in “M” shape, and merges them by abundant skip connections. By this means, our PU-M-Net can improve the flow of deep features, promote the fusion of shallow information and deep features, and enhance the utilization of initial features in phase unwrapping. We propose a structure-consideration loss function by combining the MAE and MS-SSIM loss functions, and also construct an available pertinent dataset for ESPI phase unwrapping by means of the phase shifting method. With the proposed dataset and the proposed loss function, we train the PU-M-Net successfully under a limited training condition. With the trained network, we directly get the results from the original wrapped phases. We test our method on many simulated and experimental ESPI wrapped phases, and compare it with a least-squares method, a quality-guided method, and two deep learning methods. We also compared it with the PU-M-Net trained by MSE loss function. The performance is evaluated quantitatively and qualitatively in terms of phase unwrapping and structure protection. Results demonstrate that our method can reduce speckles and protect structures in phase unwrapping, and get the better results than the compared methods. Besides, it does not require any parameter fine-turning and any pre-process or post-process procedure, even for the case of high-level speckles or high-dense wrapped phases. Moreover, it has full advantages on excellent generalization and batch performance, and can be used to process a great number of images rapidly. Indeed, it has been applied to the dynamic measurement successfully in this paper.