Figure 1.

(a) Schematic for deep learning-based holographic polarization microscopy (DL-HPM). Raw holograms are collected using a lensfree holographic microscope with a customized polarizer and analyzer. A trained neural network is used to transform the reconstructed holographic amplitude and phase images into the birefringence retardance and orientation images. (b) Schematic for single-shot computational polarized light microscopy (SCPLM). Images are collected with a four-channel pixelated polarized camera under circularly polarized illumination. Birefringent retardance and orientation channels are computed using Jones calculus, and the amplitude image is obtained by averaging the four polarization channels. SCPLM is used as the ground truth information channel, providing the network training target for DL-HPM. (c) Blind testing of DL-HPM. A new clinical sample (containing MSU crystals) collected from a deidentified patient is tested using DL-HPM. Birefringent samples are given a pseudo color using the same convention according to the compensated polarized light microscopy. Similar image quality was achieved compared to SCPLM images. Scale bar: 50μm.