Phase unwrapping plays an important role in optical phase measurements. In particular, phase unwrapping under heavy noise conditions remains an open issue. In this paper, a deep learning-based method is proposed to conduct the phase unwrapping task by combining Zernike polynomial fitting and a Swin-Transformer network. In this proposed method, phase unwrapping is regarded as a regression problem, and the Swin-Transformer network is used to map the relationship between the wrapped phase data and the Zernike polynomial coefficients. Because of the self-attention mechanism of the transformer network, the fitting coefficients can be estimated accurately even under extremely harsh noise conditions. Simulation and experimental results are presented to demonstrate the outperformance of the proposed method over the other two polynomial fitting-based methods. This is a promising phase unwrapping method in optical metrology, especially in electronic speckle pattern interferometry.
An accurate dynamic 3D deformation measurement method realized by the combination of phase-shifting speckle interferometry and speckle correlation is proposed. By converting the speckle field and the reference field into a circular polarized and linear polarized state, the three-step phase-shifting speckle interferograms and one specklegram were recorded directly and simultaneously within a single image by using a polarization camera. Then, the out-of-plane deformation was demodulated from the synchronous phase-shifting fringe patterns, and the in-plane deformation was measured by performing correlation calculations by using specklegrams with the effect of the reference field ignored. Thus, the full-field 3D deformation was obtained precisely. Experimental results demonstrated the accuracy and dynamic measurement ability of the proposed method, which is compact and feasible for actual dynamic scenes.
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