Deep neural networks enabled isotropic quantitative differential phase contrast microscopy

Isotropic quantitative differential phase contrast (iDPC) microscopy based on pupil engineering has made significant improvement in reconstructing phase image of weak phase objects. In previous researches, the pupil designs have been investigated for enhancing the data acquisition efficiency. To further improve the phase retrieval procedure in iDPC, we adapt deep neural networks to achieve isotropic phase distribution from half-pupil based quantitative differential phase contrast (qDPC) microscopy. In this study, we utilized U-net model for mapping from 1-axis phase reconstruction to 12- axis one. The results show that the deep neural network we proposed achieved expecting performance. The final testing loss value of our model after 1000 epochs of training achieved 6.7e-5 after normalized. The peak signal to noise ratio improvement is from 26dB to 30dB.