Deep Unrolled Recovery in Sparse Biological Imaging

• URL: http://arxiv.org/abs/2109.14025v1
• Date: Tue, 28 Sep 2021 20:22:44 GMT
• Title: Deep Unrolled Recovery in Sparse Biological Imaging
• Authors: Yair Ben Sahel, John P. Bryan, Brian Cleary, Samouil L. Farhi, Yonina C. Eldar
• Abstract summary: Deep algorithm unrolling is a model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning. 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.
• Score: 62.997667081978825
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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