Deep Unrolled Recovery in Sparse Biological Imaging: Achieving fast, accurate results

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing 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.