# Learned SPARCOM: Unfolded Deep Super-Resolution Microscopy

arXiv: https://arxiv.org/abs/2004.09270 · HTML: https://mlsanity.com/paper/2004.09270/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2020.

## Built on by (most cited first, 11 of 11)

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- [Can stable and accurate neural networks be computed? -- On the barriers of deep learning and Smale's 18th problem](https://mlsanity.com/paper/2101.08286/) (2021)
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- [Deep Algorithm Unrolling for Biomedical Imaging](https://mlsanity.com/paper/2108.06637/) (2021)
- [Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image](https://mlsanity.com/paper/2103.01263/) (2021)
- [Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding](https://mlsanity.com/paper/2309.06195/) (2023)
- [Deep Unfolding of Iteratively Reweighted ADMM for Wireless RF Sensing](https://mlsanity.com/paper/2106.03686/) (2021)
- [Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover](https://mlsanity.com/paper/2110.10391/) (2021)
- [Self-STORM: Deep Unrolled Self-Supervised Learning for Super-Resolution Microscopy](https://mlsanity.com/paper/2403.16974/) (2024)
- [Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospects](https://mlsanity.com/paper/2509.22692/) (2025)

## Abstract (arXiv)

The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization, but at the cost of low temporal resolution. We suggest combining SPARCOM, a recent high-performing classical method, with model-based deep learning, using the algorithm unfolding approach, to design a compact neural network incorporating domain knowledge. Our results show that we can obtain super-resolution imaging from a small number of high emitter density frames without knowledge of the optical system and across different test sets using the proposed learned SPARCOM (LSPARCOM) network. We believe LSPARCOM can pave the way to interpretable, efficient live-cell imaging in many settings, and find broad use in single-molecule localization microscopy of biological structures.