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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 developed.

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Related Experiment Video

Updated: Jun 23, 2026

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

Fluorescence spectrum estimation using multiple color images and minimum negativity constraint.

Maricor Soriano, Wilma Oblefias, Caesar Saloma

|May 23, 2009

Summary

This summary is machine-generated.

This study introduces an affordable way to turn microscopes into imaging spectrometers using a 3-chip CCD camera and color filter. The new method simplifies spectral analysis without specialized equipment, enabling detailed sample imaging.

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Last Updated: Jun 23, 2026

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Area of Science:

Spectroscopy
Microscopy
Imaging Science

Background:

Microscope-based spectrometers often require expensive, specialized optics or complex scanning mechanisms.
Existing methods can be cumbersome and limit the speed of spectral data acquisition.

Purpose of the Study:

To develop an inexpensive and simplified method for converting a standard microscope into an imaging spectrometer.
To enable pixel-by-pixel spectral analysis of samples using readily available components.

Main Methods:

Utilizes a 3-chip CCD camera and a lightly-tinted color filter for image capture.
Employs principal component analysis (PCA) on color signals from known dyes to derive basis spectra.
Applies a transformation matrix and minimum negativity constraint for spectral reconstruction.

Main Results:

Successfully converts a standard microscope into an imaging spectrometer with minimal hardware.
Achieves pixel-by-pixel spectral output without specialized optical components or scanning.
Demonstrates the technique's efficacy on fluorescence microspheres and chlorophyll in plant leaves.

Conclusions:

The presented method offers a cost-effective and accessible approach to hyperspectral imaging microscopy.
This technique simplifies spectral analysis, making it more widely applicable in various scientific fields.
The system provides a valuable tool for researchers needing detailed spectral information from microscopic samples.