Deep Unrolled Recovery in Sparse Biological Imaging

TL;DR

This paper reviews deep unrolling techniques that blend iterative algorithms with deep learning to enhance source localization in biological imaging, leveraging physics-based models for improved sparse recovery.

Contribution

It provides a comprehensive review of deep unrolling methods and demonstrates their effectiveness in biological imaging applications.

Findings

Improved source localization accuracy in biological imaging.

Deep unrolling outperforms traditional methods in sparse recovery tasks.

Framework effectively combines interpretability with deep learning performance.

Abstract

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.