Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning

Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning

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In an optical measurement system using an interferometer, a phase extracting technique from interferogram is the key issue.When the object is varying in time, the Fourier-transform method is commonly used since this method can extract a phase image from a single interferogram.However, there is a limitation, that an interferogram including closed-fringes cannot be applied.The closed-fringes appear when intervals of the background fringes are long.In some experimental setups, which need to kashayam dress change the alignments of optical components such as a 3-D optical tomographic system, the interval of the fringes cannot be controlled.

To extract the phase from the interferogram including the closed-fringes we propose the use of deep learning.A large amount of the pairs of the interferograms and phase-shift images are prepared, and the trained network, the input for which is an interferogram and the output a corresponding phase-shift image, is obtained using supervised learning.From comparisons of the extracted phase, we can demonstrate that the accuracy of the trained network is superior to iphone 12 price florida that of the Fourier-transform method.Furthermore, the trained network can be applicable to the interferogram including the closed-fringes, which is impossible with the Fourier transform method.