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Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

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Updated: Mar 10, 2026

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Low-cost Custom Fabrication and Mode-locked Operation of an All-normal-dispersion Femtosecond Fiber Laser for Multiphoton Microscopy

Published on: November 22, 2019

Deep Learning Super-Resolution Spectrometer Based on Fiber Random Laser With Ultrahigh Spectral Purity.

Jinjiang Zhao¹, Xiaomei Gao¹, Zilong Lu¹

¹School of Physics and Optoelectronic Engineering Beijing University of Technology Beijing China.

Nanophotonics (Berlin, Germany)

|March 9, 2026

Summary

This summary is machine-generated.

This study introduces a faster super-resolution spectrometer using a fiber random laser (RL) and deep learning. It significantly reduces reconstruction time for high-resolution spectral data.

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Last Updated: Mar 10, 2026

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High Resolution Phonon-assisted Quasi-resonance Fluorescence Spectroscopy

High Resolution Phonon-assisted Quasi-resonance Fluorescence Spectroscopy

Published on: June 28, 2016

Area of Science:

Spectroscopy
Optical Engineering
Applied Physics

Background:

Super-resolution spectroscopy traditionally faces speed limitations due to extensive data acquisition.
Random laser (RL) based methods offer enhanced spectral resolution but require numerous sub-resolution frames.

Purpose of the Study:

To develop a deep learning super-resolution spectrometer with significantly reduced reconstruction time.
To create a high-performance micro-nano light source for improved spectral analysis.

Main Methods:

Utilized a fiber random laser (RL) coupled to a nested fiber microcavity to generate a high-purity micro-nano light source.
Employed a convolutional neural network (CNN) for spectral reconstruction from sparse data.
Reduced the number of acquired sparse frames by threefold.

Main Results:

Achieved super-resolution spectra with up to an 80% reduction in reconstruction time.
The novel light source exhibited high spectral purity, good directivity, and a miniature size.
Demonstrated effective super-resolution recovery from significantly fewer raw frames or lower localization density.

Conclusions:

The proposed deep learning super-resolution spectrometer drastically cuts acquisition time, enabling faster spectral analysis.
This advancement promotes the development of integrated, low-cost, high-resolution, and compact spectroscopic systems.