Holographic polarization microscopy using deep learning

We present a deep learning-enabled holographic polarization microscope that only requires one polarization state to image/quantify birefringent specimen. This framework reconstructs quantitative birefringence retardance and orientation images from the amplitude/phase information obtained using a lensless holographic microscope with a pair of polarizer and analyzer. We tested this technique with various birefringent samples including monosodium urate and triamcinolone acetonide crystals to demonstrate that the deep network can accurately reconstruct the retardance and orientation image channels. This method has a simple optical design and presents a large field-of-view (>20-30mm2), which might broaden the access to advanced polarization microscopy techniques in low-resource-settings.