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Fast and accurate sCMOS noise correction for fluorescence microscopy

Author

Listed:
  • Biagio Mandracchia

    (Georgia Institute of Technology and Emory University)

  • Xuanwen Hua

    (Georgia Institute of Technology and Emory University)

  • Changliang Guo

    (Georgia Institute of Technology and Emory University)

  • Jeonghwan Son

    (Georgia Institute of Technology and Emory University)

  • Tara Urner

    (Georgia Institute of Technology and Emory University)

  • Shu Jia

    (Georgia Institute of Technology and Emory University)

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.

Suggested Citation

  • Biagio Mandracchia & Xuanwen Hua & Changliang Guo & Jeonghwan Son & Tara Urner & Shu Jia, 2020. "Fast and accurate sCMOS noise correction for fluorescence microscopy," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-13841-8
    DOI: 10.1038/s41467-019-13841-8
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    Cited by:

    1. Xuanwen Hua & Keyi Han & Biagio Mandracchia & Afsane Radmand & Wenhao Liu & Hyejin Kim & Zhou Yuan & Samuel M. Ehrlich & Kaitao Li & Corey Zheng & Jeonghwan Son & Aaron D. Silva Trenkle & Gabriel A. K, 2024. "Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Esley Torres-García & Raúl Pinto-Cámara & Alejandro Linares & Damián Martínez & Víctor Abonza & Eduardo Brito-Alarcón & Carlos Calcines-Cruz & Gustavo Valdés-Galindo & David Torres & Martina Jabloñski, 2022. "Extending resolution within a single imaging frame," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    3. Robin Diekmann & Joran Deschamps & Yiming Li & Takahiro Deguchi & Aline Tschanz & Maurice Kahnwald & Ulf Matti & Jonas Ries, 2022. "Photon-free (s)CMOS camera characterization for artifact reduction in high- and super-resolution microscopy," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Chang Qiao & Yunmin Zeng & Quan Meng & Xingye Chen & Haoyu Chen & Tao Jiang & Rongfei Wei & Jiabao Guo & Wenfeng Fu & Huaide Lu & Di Li & Yuwang Wang & Hui Qiao & Jiamin Wu & Dong Li & Qionghai Dai, 2024. "Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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