COL0RME: COvariance-based $\ell_0$ super-Resolution Microscopy with intensity Estimation

TL;DR

COL0RME introduces a novel covariance-based super-resolution microscopy method that leverages molecule fluctuations, sparse modeling, and intensity estimation to improve live-cell imaging under challenging conditions.

Contribution

The paper presents COL0RME, a new non-convex optimization approach in the covariance domain that jointly estimates super-resolution images, background, noise, and molecule intensities.

Findings

Effective super-resolution achieved with molecule fluctuation exploitation

Joint estimation of background, noise, and intensities improves biological interpretability

Applicable to real data with robust performance

Abstract

Super-resolution light microscopy overcomes the physical barriers due to light diffraction, allowing for the observation of otherwise indistinguishable subcellular entities. However, the specific acquisition conditions required by state-of-the-art super-resolution methods to achieve adequate spatio-temporal resolution are often very challenging. Exploiting molecules fluctuations allows good spatio-temporal resolution live-cell imaging by means of common microscopes and conventional fluorescent dyes. In this work, we present the method COL0RME for COvariance-based $\ell_0$ super-Resolution Microscopy with intensity Estimation. It codifies the assumption of sparse distribution of the fluorescent molecules as well as the temporal and spatial independence between emitters via a non-convex optimization problem formulated in the covariance domain. In order to deal with real data, the proposed approach also estimates background and noise statistics. It also includes a final estimation step where intensity information is retrieved, which is valuable for biological interpretation and future applications to super-resolution imaging.