PhotoniX, Volume. 4, Issue 1, 2(2023)

Enhancing image resolution of confocal fluorescence microscopy with deep learning

Boyi Huang1,†... Jia Li1,†, Bowen Yao1, Zhigang Yang1, Edmund Y. Lam2, Jia Zhang1,*, Wei Yan1,** and Junle Qu1,*** |Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China
  • 2Department of Electrical and Electronic Engineering, University of Hong Kong, Pokfulam, Hong Kong SAR, China
  • show less

    Super-resolution optical imaging is crucial to the study of cellular processes. Current super-resolution fluorescence microscopy is restricted by the need of special fluorophores or sophisticated optical systems, or long acquisition and computational times. In this work, we present a deep-learning-based super-resolution technique of confocal microscopy. We devise a two-channel attention network (TCAN), which takes advantage of both spatial representations and frequency contents to learn a more precise mapping from low-resolution images to high-resolution ones. This scheme is robust against changes in the pixel size and the imaging setup, enabling the optimal model to generalize to different fluorescence microscopy modalities unseen in the training set. Our algorithm is validated on diverse biological structures and dual-color confocal images of actin-microtubules, improving the resolution from ~ 230 nm to ~ 110 nm. Last but not least, we demonstrate live-cell super-resolution imaging by revealing the detailed structures and dynamic instability of microtubules.

    Tools

    Get Citation

    Copy Citation Text

    Boyi Huang, Jia Li, Bowen Yao, Zhigang Yang, Edmund Y. Lam, Jia Zhang, Wei Yan, Junle Qu. Enhancing image resolution of confocal fluorescence microscopy with deep learning[J]. PhotoniX, 2023, 4(1): 2

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Research Articles

    Received: Jul. 8, 2022

    Accepted: Nov. 14, 2022

    Published Online: Jul. 10, 2023

    The Author Email: Zhang Jia (julyzhang2021@163.com), Yan Wei (weiyan@szu.edu.cn), Qu Junle (jlqu@szu.edu.cn)

    DOI:10.1186/s43074-022-00077-x

    Topics