Off-the-grid covariance-based super-resolution fluctuation microscopy

TL;DR

This paper introduces an off-the-grid covariance-based super-resolution microscopy method that leverages molecular fluctuations for live-cell imaging, avoiding the limitations of traditional grid-based algorithms.

Contribution

It proposes a novel off-the-grid optimization approach that models molecular fluctuations independently, enhancing super-resolution imaging capabilities.

Findings

Enables high-resolution live-cell imaging with standard microscopes.

Reduces acquisition time and sample damage compared to traditional methods.

Provides a new framework for fluctuation-based super-resolution algorithms.

Abstract

Super-resolution fluorescence microscopy overcomes blurring arising from light diffraction, allowing the reconstruction of fine scale details in biological structures. Standard methods come at the expense of long acquisition time and/or harmful effects on the biological sample, which makes the problem quite challenging for the imaging of body cells. A promising new avenue is the exploitation of molecules fluctuations, allowing live-cell imaging with good spatio-temporal resolution through common microscopes and conventional fluorescent dyes. Several numerical algorithms have been developed in the literature and used for fluctuant time series. These techniques are developed within the discrete setting, namely the super-resolved image is defined on a finer grid than the observed images. On the contrary, gridless optimisation does not rely on a fine grid and is rather an optimisation of Dirac measures in number, amplitudes and positions. In this work, we present an off-the-grid problem accounting for the independence of fluctuations.