Super resolution label-free dark-field microscopy by deep learning
Ming Lei, Junxiang Zhao, Junxiao Zhou, Hongki Lee, Qianyi Wu, Zachary Burns, Guanghao Chen, Zhaowei Liu
Abstract
Dark-field microscopy (DFM) is a powerful label-free and high-contrast imaging technique due to its ability to reveal features of transparent specimens with inhomogeneities. However, owing to the Abbe's diffraction limit, fine structures at sub-wavelength scale are difficult to resolve. In this work, we report a single image super resolution DFM scheme using a convolutional neural network (CNN). A U-net based CNN is trained with a dataset which is numerically simulated based on the forward physical model of the DFM. The forward physical model described by the parameters of the imaging setup connects the object ground truths and dark field images. With the trained network, we demonstrate super resolution dark field imaging of various test samples with twice resolution improvement. Our technique illustrates a promising deep learning approach to double the resolution of DFM without any hardware modification.