Deep Unrolled Recovery in Sparse Biological Imaging
This is an incremental review focusing on enhancing source localization in biological imaging for researchers in that domain.
The paper reviews deep algorithm unrolling, a model-based approach combining iterative algorithms with deep learning, and demonstrates its application in biological imaging to improve source localization.
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.