SCMOS Noise-Corrected Superresolution Reconstruction Algorithm for Structured Illumination Microscopy

Bo Zhou,Xiaoshuai Huang,Liangyi Chen,Junchao Fan

doi:10.3390/photonics9030172

Abstract

Structured illumination microscopy (SIM) is widely applied due to its high temporal and spatial resolution imaging ability. sCMOS cameras are often used in SIM due to their superior sensitivity, resolution, field of view, and frame rates. However, the unique single-pixel-dependent readout noise of sCMOS cameras may lead to SIM reconstruction artefacts and affect the accuracy of subsequent statistical analysis. We first established a nonuniform sCMOS noise model to address this issue, which incorporates the single-pixel-dependent offset, gain, and variance based on the SIM imaging process. The simulation indicates that the sCMOS pixel-dependent readout noise causes artefacts in the reconstructed SIM superresolution (SR) image. Thus, we propose a novel sCMOS noise-corrected SIM reconstruction algorithm derived from the imaging model, which can effectively suppress the sCMOS noise-related reconstruction artefacts and improve the signal-to-noise ratio (SNR).

Highlights

Superresolution (SR) microscopy enables biological researchers to see nanoscale images of intracellular structures
To minimize these artefacts caused by readout noise, we propose an scientific-grade complementary metal-oxide-semiconductor (sCMOS) noise model based on structured illumination microscopy (SIM) imaging and present a novel noisecorrected algorithm for SIM reconstruction
The statistical box plot (Figure 3d) shows that the signal-to-noise ratio (SNR) of the SR image reconstructed by the conventional algorithm is ~16.8 and improved to ~17.7 by the proposed sCMOS noise-corrected SIM reconstruction algorithm, which is consistent with the phenomenon of punctiform artefact elimination above

Summary

Introduction

Superresolution (SR) microscopy enables biological researchers to see nanoscale images of intracellular structures. Various SR fluorescence microscopy techniques, such as stimulated emission depletion (STED) [1–3], photoactivated localization microscopy (PALM) [4–6], stochastic optical reconstruction microscopy (STORM) [7–9], and structured illumination microscopy (SIM) [10–15], have come to the fore during the past 20 years. These SR technologies can break the optical diffraction limit and achieve a spatial resolution of approximately 20~100 nm compared to the ~200 nm of conventional microscopes. Scientific-grade complementary metal-oxide-semiconductor (sCMOS) cameras are a better choice for offering sufficient quantum efficiency and much faster readout speed, significantly increasing the data acquisition rate and improving the temporal resolution [20–22]. With the sCMOS camera, Hessian-SIM was developed to increase the temporal resolution and image rapidly moving vesicles or loops in the endoplasmic reticulum with a temporal resolution of 188 Hz [23]

Methods

Results

Discussion

Conclusion