Deep learning based image quality improvement of a light-field microscope integrated with an epi-fluorescence microscope

Abstract

Light-field three-dimensional (3D) fluorescence microscopes can acquire 3D fluorescence images in a single shot, and followed numerical reconstruction can realize cross-sectional imaging at an arbitrary depth. The typical configuration that uses a lens array and a single image sensor has the trade-off between depth information acquisition and spatial resolution of each cross-sectional image. The spatial resolution of the reconstructed image degrades when depth information increases. In this paper, we use U-net as a deep learning model to improve the quality of reconstructed images. We constructed an optical system that integrates a light-field microscope and an epifluorescence microscope, which acquire the light-field data and high-resolution two-dimensional images, respectively. The high-resolution images from the epifluorescence microscope are used as ground-truth images for the training dataset for deep learning. The experimental results using fluorescent beads with a size of 10 µm and cultured tobacco cells showed significant improvement in the reconstructed images. Furthermore, time-lapse measurements were demonstrated in tobacco cells to observe the cell division process.