Fast and accurate sCMOS noise correction for fluorescence microscopy

• Type of Material: Article
• Authors: Biagio Mandracchia, Georgia Institute of Technology; Xuanwen Hua, Georgia Institute of Technology; Changliang Guo, Georgia Institute of Technology; Jeonghwan Son, Georgia Institute of Technology; Tara Urner, Georgia Institute of Technology; Shu Jia, Emory University
• Language: English
• Date: 2020-12-01
• Publisher: Nature Research (part of Springer Nature): Fully open access journals
• Publication Version: Final Published Version
• Copyright Statement: © 2020, The Author(s). CC BY 4.0
• Final Published Version (URL): http://dx.doi.org/10.1038/s41467-019-13841-8
• Title of Journal or Parent Work: Nature Communications
• ISSN: 2041-1723
• Volume: 11
• Issue: 1
• Start Page: 94
• End Page: 94
• Grant/Funding Information: T. Urner is supported by the National Science Foundation Graduate Fellowship.; We acknowledge the support of the National Institutes of Health grant R35GM124846, and the National Science Foundation grants CBET1604565 and EFMA1830941.; This research project was supported in part by the Emory University Integrated Cellular Imaging Microscopy Core and by PHS Grant UL1TR000454 from the Clinical and Translational Science Award Program, National Institutes of Health, and National Center for Advancing Translational Sciences.
• Keywords: physics; biomedical engineering; Microscopy; Fluorescence imaging; electrons
• Research Categories: Engineering, Biomedical; Biophysics, General

Abstract

The rapid development of scientific CMOS (sCMOS) technology has greatly advanced optical microscopy for biomedical research with superior sensitivity, resolution, field-of-view, and frame rates. However, for sCMOS sensors, the parallel charge-voltage conversion and different responsivity at each pixel induces extra readout and pattern noise compared to charge-coupled devices (CCD) and electron-multiplying CCD (EM-CCD) sensors. This can produce artifacts, deteriorate imaging capability, and hinder quantification of fluorescent signals, thereby compromising strategies to reduce photo-damage to live samples. Here, we propose a content-adaptive algorithm for the automatic correction of sCMOS-related noise (ACsN) for fluorescence microscopy. ACsN combines camera physics and layered sparse filtering to significantly reduce the most relevant noise sources in a sCMOS sensor while preserving the fine details of the signal. The method improves the camera performance, enabling fast, low-light and quantitative optical microscopy with video-rate denoising for a broad range of imaging conditions and modalities.