Single-shot computational spatial light interference microscopy (SSC-SLIM)

Abstract

Quantitative Phase Imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method. However, as a phase-shifting technique, SLIM is limited in acquisition rate to at most 15 fps. On the other hand, Diffraction Phase Microscopy (DPM) is such a method, with the advantage of being common-path. However, laser-based DPM systems are plagued by spatial noise due to speckles and multiple reflections. Here, we propose using deep learning to produce SLIM-quality phase maps from DPM, single shot, images. We constructed a deep learning model based on U-Net and trained on over 1,000 pairs of DPM and SLIM images. From the test set, we observed that the model learned to remove the speckles in DPM and overcame the background phase noise. We implemented the neural network inference into the live acquisition software and allows us to acquire single-shot DPM images and infer from them SLIM images in real time.