  Light-field microscopes are able to capture spatial and angular information
of incident light rays. This allows reconstructing 3D locations of neurons from
a single snap-shot.In this work, we propose a model-inspired deep learning
approach to perform fast and robust 3D localization of sources using
light-field microscopy images. This is achieved by developing a deep network
that efficiently solves a convolutional sparse coding (CSC) problem to map
Epipolar Plane Images (EPI) to corresponding sparse codes. The network
architecture is designed systematically by unrolling the convolutional
Iterative Shrinkage and Thresholding Algorithm (ISTA) while the network
parameters are learned from a training dataset. Such principled design enables
the deep network to leverage both domain knowledge implied in the model, as
well as new parameters learned from the data, thereby combining advantages of
model-based and learning-based methods. Practical experiments on localization
of mammalian neurons from light-fields show that the proposed approach
simultaneously provides enhanced performance, interpretability and efficiency.
