  Light field microscopy is a compact solution to high-speed 3D fluorescence
imaging. Usually, we need to do 3D deconvolution to the captured raw data.
Although there are deep neural network methods that can accelerate the
reconstruction process, the model is not universally applicable for all system
parameters. Here, we develop AutoDeconJ, a GPU accelerated ImageJ plugin for
4.4x faster and accurate deconvolution of light field microscopy data. We
further propose an image quality metric for the deconvolution process, aiding
in automatically determining the optimal number of iterations with higher
reconstruction accuracy and fewer artifacts
