Abstract
Dynamic quantitative phase imaging provides an effective solution for measuring the dynamic process of time-varying objects, such as biological samples, fluids, and flexible materials. However, there have been no effective approaches considering the spatial–temporal information of the dynamic process. Here, we report Ynet convolutional long short-term memory (Ynet-ConvLSTM) neural network; it learns the spatial features of the measured object that changes continuously along the time axis of a dynamic process by exploiting the known information of the interferogram sequence and phase images. According to our results, Ynet-ConvLSTM network improved the accuracy of phase image reconstruction in different dynamic circumstances.
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