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3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture

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Abstract

The extraction of absolute phase from an interference pattern is a key step for 3D deformation measurement in digital holographic interferometry (DHI) and is an ill-posed problem. Estimating the absolute unwrapped phase becomes even more challenging when the obtained wrapped phase from the interference pattern is noisy. In this paper, we propose a novel multitask deep learning approach for phase reconstruction and 3D deformation measurement in DHI, referred to as TriNet, that has the capability to learn and perform two parallel tasks from the input image. The proposed TriNet has a pyramidal encoder–two-decoder framework for multi-scale information fusion. To our knowledge, TriNet is the first multitask approach to accomplish simultaneous denoising and phase unwrapping of the wrapped phase from the interference fringes in a single step for absolute phase reconstruction. The proposed architecture is more elegant than recent multitask learning methods such as Y-Net and state-of-the-art segmentation approaches such as UNet$++$. Further, performing denoising and phase unwrapping simultaneously enables deformation measurement from the highly noisy wrapped phase of DHI data. The simulations and experimental comparisons demonstrate the efficacy of the proposed approach in absolute phase reconstruction and 3D deformation measurement with respect to the existing conventional methods and state-of-the-art deep learning methods.

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Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

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