RLP-Net: A Recursive Light Propagation Network for 3-D Virtual Refocusing

Abstract

High-speed optical 3-D fluorescence microscopy is an essential tool for capturing the rapid dynamics of biological systems such as cellular signaling and complex movements. Designing such an optical system is constrained by the inherent trade-off among resolution, speed, and noise which comes from the limited number of photons that can be collected. In this paper, we propose a recursive light propagation network (RLP-Net) that infers the 3-D volume from two adjacent 2-D wide-field fluorescence images via virtual refocusing. Specifically, we propose a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction. This recursive inference scheme reflects that the law of physics for the light propagation remains spatially invariant and therefore a fixed function (i.e., a neural network) for a short distance light propagation can be recursively applied for a longer distance light propagation. Experimental results show that the proposed method can faithfully reconstruct the 3-D volume from two planes in terms of both quantitative measures and visual quality. The source code used in the paper is available at https://github.com/NICALab/rlpnet.