A deep learning-based concept for quantitative phase imaging upgrade of bright-field microscope

In this paper, we propose an approach that combines wavefront encoding and convolutional neuronal network (CNN)-based decoding for quantitative phase imaging (QPI). Encoding is realized by defocusing, and decoding by CNN trained on simulated datasets. We have demonstrated that based on the proposed approach of creating the dataset, it is possible to overcome the typical pitfall of CNN learning, such as the shortage of reliable data. In the proposed data flow, CNN training is performed on simulated data, while CNN application is performed on real data. Our approach is benchmarked in real-life experiments with a digital holography approach. Our approach is purely software-based: the QPI upgrade of a bright-field microscope does not require extra optical components such as reference beams or spatial light modulators.