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Related Experiment Video

Updated: Jun 6, 2025

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Measurement of Scattering Nonlinearities from a Single Plasmonic Nanoparticle

Published on: January 3, 2016

Deep Learning Assisted Plasmonic Dark-Field Microscopy for Super-Resolution Label-Free Imaging.

Ming Lei¹, Junxiang Zhao¹, Ayse Z Sahan^2,3

¹Department of Electrical and Computer Engineering, University of California-San Diego, 9500 Gilman Drive, La Jolla, California 92093, United States.

|November 25, 2024

Summary

This summary is machine-generated.

Deep-learning assisted plasmonic dark-field microscopy (DAPD) enhances imaging resolution for label-free specimens. This novel technique offers a compact, super-resolution alternative to traditional dark-field microscopy (DFM).

Keywords:

Super resolution dark field microscopy deep learning surface plasmon polaritons

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Area of Science:

Optics and Photonics
Biomedical Imaging
Materials Science

Background:

Dark-field microscopy (DFM) provides high-contrast imaging for label-free specimens.
Traditional DFM is limited by diffraction and requires complex optical alignment.
There is a need for enhanced resolution in label-free microscopy techniques.

Purpose of the Study:

To develop a deep-learning assisted plasmonic dark-field microscopy (DAPD) technique.
To achieve super-resolution imaging of label-free specimens.
To provide a compact and improved alternative to conventional DFM.

Main Methods:

Fabrication of a plasmonic dark-field (PDF) substrate utilizing surface plasmon polaritons (SPPs).
Illumination of specimens using SPPs on the designed substrate.
Deep-learning image reconstruction using a pretrained convolutional neural network (CNN) on simulated data.

Main Results:

Demonstrated a 2.8-fold resolution enhancement on various label-free objects.
Achieved single-frame super-resolution imaging.
Showcased the potential for further resolution improvements.

Conclusions:

DAPD offers a significant enhancement in spatial resolution compared to traditional DFM.
The technique is a compact and effective alternative for high-resolution label-free imaging.
Deep learning combined with plasmonics opens new avenues for advanced microscopy.