Journal
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION
Volume 38, Issue 3, Pages 321-327Publisher
Optica Publishing Group
DOI: 10.1364/JOSAA.412433
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Funding
- National Natural Science Foundation of China [11374130, 11474134,11874184]
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A new method for decoupling dual-wavelength interferograms using deep learning is proposed, which requires only three randomly phase-shifted interferograms to obtain the desired phase information.
In dual-wavelength interferometry, the key issue is how to efficiently retrieve the phases at each wavelength using the minimum number of wavelength-multiplexed interferograms. To address this problem, a new dual-wavelength interferogram decoupling method with the help of deep learning is proposed in this study. This method requires only three randomly phase-shifted dual-wavelength interferograms. With a well-trained deep neural network, one can obtain three interferograms with arbitrary phase shifts at each wavelength. Using these interferograms, the wrapped phases of a single wavelength can be extracted, respectively, via an iterative phase retrieval algorithm, and then the phases at different synthetic beat wavelengths can be calculated. The feasibility and applicability of the proposed method are demonstrated by simulation experiments of the spherical cap and red blood cell, respectively. This method will provide a solution for the problem of phase retrieval in multiwavelength interferometry. (c) 2021 Optical Society of America
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