COL0RME: Super-resolution microscopy based on sparse blinking/fluctuating fluorophore localization and intensity estimation. (16th February 2022)

• Record Type: Journal Article
• Authors: Stergiopoulou, Vasiliki; Calatroni, Luca; de Morais Goulart, Henrique; Schaub, Sébastien; Blanc-Féraud, Laure
• Is Part Of: Biological imaging. Volume 2(2022)
• Publication Date: 2022-02-16
• Subjects: Fluorescence microscopy -- SOFI method -- sparse optimization -- super-resolution; Imaging systems in biology -- Periodicals; 570.28
• DOI: 10.1017/S2633903X22000010
• Languages: English
• ISSNs: 2633-903X

Abstract

Abstract: To overcome the physical barriers caused by light diffraction, super-resolution techniques are often applied in fluorescence microscopy. State-of-the-art approaches require specific and often demanding acquisition conditions to achieve adequate levels of both spatial and temporal resolution. Analyzing the stochastic fluctuations of the fluorescent molecules provides a solution to the aforementioned limitations, as sufficiently high spatio-temporal resolution for live-cell imaging can be achieved using common microscopes and conventional fluorescent dyes. Based on this idea, we present COL0RME, a method for covariance-based $ {\mathrm{\ell}}_0 $ super-resolution microscopy with intensity estimation, which achieves good spatio-temporal resolution by solving a sparse optimization problem in the covariance domain and discuss automatic parameter selection strategies. The method is composed of two steps: the former where both the emitters' independence and the sparse distribution of the fluorescent molecules are exploited to provide an accurate localization; the latter where real intensity values are estimated given the computed support. The paper is furnished with several numerical results both on synthetic and real fluorescence microscopy images and several comparisons with state-of-the art approaches are provided. Our results show that COL0RME outperforms competing methods exploiting analogously temporal fluctuations; in particular, it achieves better localization, reduces background artifacts, and avoids fine parameter tuning.