Jointly super-resolved and optically sectioned Bayesian reconstruction method for structured illumination microscopy

TL;DR

BOSSA-SIM is a Bayesian reconstruction method that jointly achieves super-resolution and optical sectioning in structured illumination microscopy, effective for in vivo imaging and validated with simulations and experimental data.

Contribution

This work introduces BOSSA-SIM, a novel unsupervised Bayesian approach for joint super-resolution and optical sectioning in SIM, applicable to moving objects like retinal imaging.

Findings

Performs favorably against state-of-the-art methods in simulations.

Successfully reconstructs super-resolved, optically sectioned images from experimental data.

Effective for in vivo retinal imaging with moving samples.

Abstract

Structured Illumination Microscopy (SIM) is an imaging technique for achieving both super-resolution (SR) and optical sectioning (OS) in wide-field microscopy. It consists in illuminating the sample with periodic patterns at different orientations and positions. The resulting images are then processed to reconstruct the observed object with SR and/or OS. In this work, we present BOSSA-SIM, a general-purpose SIM reconstruction method, applicable to moving objects such as encountered in in vivo retinal imaging, that enables SR and OS jointly in a fully unsupervised Bayesian framework. By modeling a 2-layer object composed of an in-focus layer and a defocused layer, we show that BOSSA-SIM is able to jointly reconstruct them so as to get a super-resolved and optically sectioned in-focus layer. The achieved performance, assessed quantitatively by simulations for several noise levels, compares favorably with a state-of-the-art method. Finally, we validate our method on open-access experimental microscopy data.