# DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy

arXiv: https://arxiv.org/abs/2107.02281 · HTML: https://mlsanity.com/paper/2107.02281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 36 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 2 of 2)

- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SPARCOM: Sparsity Based Super-Resolution Correlation Microscopy](https://mlsanity.com/paper/1707.09255/) (2017)

## Abstract (arXiv)

In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super Resolution (SR) plays an important role in this field since it allows to go beyond the intrinsic light diffraction limit. In this work, we propose a deep learning-based algorithm for precise molecule localization of high density frames acquired by SMLM techniques whose ℓ2-based loss function is regularized by positivity and ℓ0-based constraints. The ℓ0 is relaxed through its Continuous Exact ℓ0 (CEL0) counterpart. The arising approach, named DeepCEL0, is parameter-free, more flexible, faster and provides more precise molecule localization maps if compared to the other state-of-the-art methods. We validate our approach on both simulated and real fluorescence microscopy data.