LSPARCOM: Deep unfolded super-resolution microscopy

Gili Dardikman-Yoffe*, Yonina C. Eldar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


The use of photo-activated fluorescent molecules to create long sequences of low emitter-density diffraction-limited images enables high-precision emitter localization. However, this is achieved at the cost of lengthy imaging times, limiting temporal resolution. In recent years, a variety of approaches have been suggested to reduce imaging times, ranging from classical optimization and statistical algorithms to deep learning methods. Classical methods often rely on prior knowledge of the optical system and require heuristic adjustment of parameters or do not lead to good enough performance. Deep learning methods proposed to date tend to suffer from poor generalization ability outside the specific distribution they were trained on, and tend to lead to black-box solutions that are hard to interpret. In this paper, we suggest combining a recent high-performing classical method, SPARCOM, with model-based deep learning, using the algorithm unfolding approach which relies on an iterative algorithm to design a compact neural network considering domain knowledge. We show that the resulting network, Learned SPARCOM (LSPARCOM), requires far fewer layers and parameters, and can be trained on a single field of view. Nonetheless it yields comparable or superior results to those obtained by SPARCOM with no heuristic parameter determination or explicit knowledge of the point spread function, and is able to generalize better than standard deep learning techniques. Thus, we believe LSPARCOM will find broad use in single molecule localization microscopy of biological structures, and pave the way to interpretable, efficient live-cell imaging in a broad range of settings.

Original languageEnglish
Title of host publicationSingle Molecule Spectroscopy and Superresolution Imaging XIV
EditorsIngo Gregor, Felix Koberling, Rainer Erdmann
ISBN (Electronic)9781510641358
StatePublished - 2021
Externally publishedYes
EventSingle Molecule Spectroscopy and Superresolution Imaging XIV 2021 - Virtual, Online, United States
Duration: 6 Mar 202111 Mar 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceSingle Molecule Spectroscopy and Superresolution Imaging XIV 2021
Country/TerritoryUnited States
CityVirtual, Online


  • Algorithm unfolding
  • Interpretable AI
  • Single molecule localization microscopy
  • Super resolution microscopy


Dive into the research topics of 'LSPARCOM: Deep unfolded super-resolution microscopy'. Together they form a unique fingerprint.

Cite this