Deep Learning For Light Field Microscopy Using Physics-Based Models

Abstract

Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information in a single 2D image. LFM is attractive because of its relatively simple implementation and fast acquisition rate. However, classic 3D reconstruction typically suffers from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we propose a new physics-based learning approach to improve the performance of the reconstruction under realistic conditions, these being lack of training data, background noise, and high data dimensionality. First, we propose a novel description of the system using a linear convolutional neural network. This description is complemented by a method that compacts the number of views of the acquired light field. Then, this model is used to solve the inverse problem under two scenarios. If labelled data is available, we train an end-to-end network that uses the Learned Iterative Shrinkage and Thresholding Algorithm (LISTA). If no labelled data is available, we propose an unsupervised technique that uses only unlabelled data to train LISTA by making use of Wasserstein Generative Adversarial Networks (WGANs). We experimentally show that our approach performs better than classic strategies in terms of artifact reduction and image quality.