Laser & Optoelectronics Progress, Volume. 61, Issue 16, 1611002(2024)

Advances in Deep Learning for Super-Resolution Microscopy(Invited)

Xinyi Lu1,2, Yu Huang3, Zitong Zhang4, Tianxiao Wu1,2, Hongjun Wu1,2, Yongtao Liu1,2、*, Zhong Fang3、**, Chao Zuo1,2、***, and Qian Chen1,2
Author Affiliations
  • 1Smart Computational Imaging Laboratory, College of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • 2Key Laboratory of Spectral Imaging & Intelligent Sense, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • 3School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
  • 4Infection Management Department of Shenzhen Sami Medical Center (Shenzhen Fourth People's Hospital), Shenzhen 518118, Guangdong, China
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    Super-resolution microscopy imaging technology surpasses the diffraction limit of traditional microscopes, thereby offering unprecedented detail and allowing observation of the microscopic world below this limit. This advancement remarkably promotes developments in various fields such as biomedical, cytology, and neuroscience. However, existing super-resolution microscopy techniques have certain drawbacks, such as slow imaging speed, artifacts in reconstructed images, considerable light damage to biological samples, and low axial resolution. Recently, with advancements in artificial intelligence, deep learning has been applied to address these issues, overcoming the limitations of super-resolution microscopy imaging technology. This study examines the shortcomings of mainstream super-resolution microscopy imaging technology, summarizes how deep learning optimizes this technology, and evaluates the effectiveness of various networks based on the principles of super-resolution microscopy. Moreover, it analyzes the challenges of applying deep learning to this technology and explores future development prospects.

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    Xinyi Lu, Yu Huang, Zitong Zhang, Tianxiao Wu, Hongjun Wu, Yongtao Liu, Zhong Fang, Chao Zuo, Qian Chen. Advances in Deep Learning for Super-Resolution Microscopy(Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(16): 1611002

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 7, 2024

    Accepted: Jul. 2, 2024

    Published Online: Aug. 19, 2024

    The Author Email: Liu Yongtao (Yongtao.Liu@njust.edu.cn), Fang Zhong (fangzhong@njust.edu.cn), Zuo Chao (zuochao@njust.edu.cn)

    DOI:10.3788/LOP241455

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