# Deep Unrolled Recovery in Sparse Biological Imaging

arXiv: https://arxiv.org/abs/2109.14025 · HTML: https://mlsanity.com/paper/2109.14025/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 43 references that are in the map.
- First posted on arXiv 28 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound](https://mlsanity.com/paper/1811.08252/) (2018)
- [Deep learning in ultrasound imaging](https://mlsanity.com/paper/1907.02994/) (2019)
- [Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy](https://mlsanity.com/paper/2004.09270/) (2020)
- [Learned super resolution ultrasound for improved breast lesion characterization](https://mlsanity.com/paper/2107.05270/) (2021)
- [SPARCOM: Sparsity Based Super-Resolution Correlation Microscopy](https://mlsanity.com/paper/1707.09255/) (2017)
- [Training Convolutional Neural Networks and Compressed Sensing End-to-End for Microscopy Cell Detection](https://mlsanity.com/paper/1810.03075/) (2018)
- [Model-inspired Deep Learning for Light-Field Microscopy with Application to Neuron Localization](https://mlsanity.com/paper/2103.06164/) (2021)

## Built on by (most cited first, 10 of 10)

- [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022)
- [Theoretical Perspectives on Deep Learning Methods in Inverse Problems](https://mlsanity.com/paper/2206.14373/) (2022)
- [Physics Embedded Machine Learning for Electromagnetic Data Imaging](https://mlsanity.com/paper/2207.12607/) (2022)
- [Glimpse: Generalized Locality for Scalable and Robust CT](https://mlsanity.com/paper/2401.00816/) (2024)
- [Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding](https://mlsanity.com/paper/2309.06195/) (2023)
- [Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Acceleration](https://mlsanity.com/paper/2304.14491/) (2023)
- [Unrolled algorithms for group synchronization](https://mlsanity.com/paper/2207.09418/) (2022)
- [A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution](https://mlsanity.com/paper/2306.06378/) (2023)
- [Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy](https://mlsanity.com/paper/2403.16974/) (2024)
- [mSQUID: Model-Based Leanred Modulo Recovery at Low Sampling Rates](https://mlsanity.com/paper/2510.18729/) (2025)

## Abstract (arXiv)

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.