Abstract
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capable of capturing optically encoded dynamic signals. Then, we detail the data processing and model training of a view-channel-depth (VCD) neural network, which enables instant 3D image reconstruction from a single 2D light-field snapshot. Finally, we describe VCD-LFM imaging of several model organisms and demonstrate image-based quantitative studies on neural activities and cardio-hemodynamics. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).1.