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4 October 2024 Super resolution label-free imaging by deep learning assisted plasmonic dark-field microscopy
Ming Lei, Junxiang Zhao, Junxiao Zhou, Hongki Lee, Qianyi Wu, Guanghao Chen, Zhaowei Liu
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Proceedings Volume 13117, Enhanced Spectroscopies and Nanoimaging 2024; 1311710 (2024) https://doi.org/10.1117/12.3028327
Event: Nanoscience + Engineering, 2024, San Diego, California, United States
Abstract
Dark-field microscopy (DFM) is a widely used imaging tool due to its high-contrast capability in imaging label-free specimens. However, traditional DFM requires optical alignment to block the oblique illumination and the resolution is diffraction-limited to wavelength scale. In this work, we present a single frame super resolution method using plasmonic dark-field microscopy (PDF) and deep learning image reconstruction algorithm. Based on our framework, we demonstrated more than 2.5 times resolution enhancement on various objects. We highlight the potential of our technique as a compact alternative of traditional DFM with enhanced spatial resolution.
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Ming Lei, Junxiang Zhao, Junxiao Zhou, Hongki Lee, Qianyi Wu, Guanghao Chen, and Zhaowei Liu "Super resolution label-free imaging by deep learning assisted plasmonic dark-field microscopy", Proc. SPIE 13117, Enhanced Spectroscopies and Nanoimaging 2024, 1311710 (4 October 2024); https://doi.org/10.1117/12.3028327
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KEYWORDS
Plasmonics
Super resolution
Microscopy
Deep learning
Biomedical optics
Emissivity
Neural networks
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