Photonics (Oct 2024)

Real-Time Resolution Enhancement of Confocal Laser Scanning Microscopy via Deep Learning

  • Zhiying Cui,
  • Yi Xing,
  • Yunbo Chen,
  • Xiu Zheng,
  • Wenjie Liu,
  • Cuifang Kuang,
  • Youhua Chen

DOI
https://doi.org/10.3390/photonics11100983
Journal volume & issue
Vol. 11, no. 10
p. 983

Abstract

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Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging of biological cells. However, the imaging resolution of conventional confocal technology is limited by diffraction, and more complex optical principles and expensive optical-mechanical structures are usually required to improve the resolution. This study proposed a deep residual neural network algorithm that can effectively improve the imaging resolution of the confocal microscopy in real time. The reliability and real-time performance of the algorithm were verified through imaging experiments on different biological structures, and an imaging resolution of less than 120 nm was achieved in a more cost-effective manner. This study contributes to the real-time improvement of the imaging resolution of confocal microscopy and expands the application scenarios of confocal microscopy in biological imaging.

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