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In optical metrology, the output is usually in the form of a fringe pattern, from which a phase map can be generated and phase information can be converted into the desired parameters. This paper proposes an end-to-end method of fringe phase extraction based on the neural network. This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods. The resultsdoi:10.3390/s21051664 pmid:33670957 pmcid:PMC7957713 fatcat:npptgoqdvrhs5cfog5qv5njbh4