arXiv Open Access 2021

Deep Unrolled Recovery in Sparse Biological Imaging

Yair Ben Sahel John P. Bryan Brian Cleary Samouil L. Farhi Yonina C. Eldar

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Abstrak

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.

Topik & Kata Kunci

cs.CV cs.IT cs.LG eess.SP

Penulis (5)

Yair Ben Sahel

John P. Bryan

Brian Cleary

Samouil L. Farhi

Yonina C. Eldar

Format Sitasi

APA MLA BibTeX

Sahel, Y.B., Bryan, J.P., Cleary, B., Farhi, S.L., Eldar, Y.C. (2021). Deep Unrolled Recovery in Sparse Biological Imaging. https://arxiv.org/abs/2109.14025

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Informasi Jurnal

Tahun Terbit: 2021
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓