  Deep algorithm unrolling has emerged as a powerful model-based approach to
develop deep architectures that combine the interpretability of iterative
algorithms with the performance gains of supervised deep learning, especially
in cases of sparse optimization. This framework is well-suited to applications
in biological imaging, where physics-based models exist to describe the
measurement process and the information to be recovered is often highly
structured. Here, we review the method of deep unrolling, and show how it
improves source localization in several biological imaging settings.
