Laser & Optoelectronics Progress, Volume. 58, Issue 24, 2400007(2021)
Progress on Applications of Deep Learning in Super-Resolution Microscopy Imaging
Researchers can now identify dynamic activities in living cells at the nanoscale with remarkable temporal and spatial resolution because of the advancement in fluorescent super-resolution imaging. Traditional super-resolution microscopy requires high-power lasers or numerous raw images to rebuild a single super-resolution image, limiting its applications in live cell dynamic imaging. In many ways, deep learning-driven super-resolution imaging approaches break the bottleneck of existing super-resolution imaging technology. In this review, we explain the theory of optical super-resolution imaging systems and discuss their limitations. Furthermore, we outline the most recent advances and applications of deep learning in the field of super-resolution imaging, as well as address challenging difficulties and future possibilities.
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Qingshuang Lu, Luhong Jin, Yingke Xu. Progress on Applications of Deep Learning in Super-Resolution Microscopy Imaging[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2400007
Category: Reviews
Received: Apr. 21, 2021
Accepted: Jun. 19, 2021
Published Online: Dec. 7, 2021
The Author Email: Jin Luhong (lhjin@zju.edu.cn)