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Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
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3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture.

Krishna Sumanth Vengala, Naveen Paluru, Rama Krishna Sai Subrahmanyam Gorthi

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |February 24, 2022
    PubMed
    Summary

    This study introduces TriNet, a novel deep learning method for digital holographic interferometry (DHI). TriNet accurately reconstructs absolute phase and measures 3D deformation, even from noisy interference patterns.

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    Area of Science:

    • Optics and Photonics
    • Computer Vision
    • Metrology

    Background:

    • Absolute phase extraction is crucial for 3D deformation measurement in digital holographic interferometry (DHI).
    • This process is ill-posed and challenging, especially with noisy interference patterns.

    Purpose of the Study:

    • To propose a novel multitask deep learning approach, TriNet, for simultaneous phase reconstruction and 3D deformation measurement in DHI.
    • To address the challenges of noisy data and ill-posed phase unwrapping in DHI.

    Main Methods:

    • A pyramidal encoder-two-decoder framework for multi-scale information fusion.
    • A multitask deep learning architecture (TriNet) performing simultaneous denoising and phase unwrapping.
    • Application to digital holographic interferometry (DHI) for absolute phase reconstruction.

    Main Results:

    • TriNet achieves simultaneous denoising and phase unwrapping in a single step for absolute phase reconstruction.
    • The method effectively reconstructs absolute phase and measures 3D deformation from highly noisy DHI data.
    • Simulations and experiments confirm TriNet's efficacy compared to conventional and deep learning methods.

    Conclusions:

    • TriNet offers an elegant and effective solution for absolute phase reconstruction and 3D deformation measurement in DHI.
    • The simultaneous denoising and unwrapping capability makes it robust for noisy interference patterns.
    • This multitask approach advances DHI analysis and measurement accuracy.