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.

Plant phenotyping relevance

植物細胞の3D蛍光画像再構成を深層学習で改善する光学・画像解析手法を開発し、タバコ細胞の細胞分裂をタイムラプス観察しているため、植物状態の取得方法が中心です。

Code and data availability

The paper's phenotype-relevant assets (light-field/epi-fluorescence image training datasets of fluorescent beads and tobacco cells, U-net model, and analysis code) are not publicly available; the authors state data may be obtained upon reasonable request. The only public URL in the article (U-Net reference site) is a C

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