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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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Related Experiment Video

Updated: Oct 20, 2025

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Computational framework for generating large panoramic super-resolution images from localization microscopy.

Yue Du¹, Chenze Wang², Chen Zhang¹

¹Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.

Biomedical Optics Express

|September 13, 2021

Summary

This summary is machine-generated.

We developed NanoStitcher, a new image mosaic method, to combine super-resolution microscopy and digital pathology. NanoStitcher improves panoramic image generation for advanced biomedical research.

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Area of Science:

Biomedical research
Digital pathology
Microscopy

Background:

Super-resolution localization microscopy offers detailed biological insights.
Integrating microscopy with pathology requires generating large panoramic images from smaller, overlapping high-resolution images.
Existing image mosaic methods are inadequate for super-resolution microscopy data.

Purpose of the Study:

To develop and evaluate a novel computational framework and image mosaic method for super-resolution digital pathology.
To address the limitations of current methods in creating seamless panoramic images from super-resolution microscopy data.

Main Methods:

Development of a computational framework and image mosaic algorithm named NanoStitcher.
Generation of ground truth datasets for quantitative evaluation.
Performance comparison using simulated and experimental super-resolution microscopy datasets against established methods.

Main Results:

NanoStitcher demonstrated superior performance compared to two representative image mosaic methods.
The developed framework provides a robust solution for generating high-quality panoramic images from super-resolution data.

Conclusions:

The proposed NanoStitcher method enhances the integration of super-resolution microscopy and digital pathology.
This advancement facilitates the development of super-resolution digital pathology and aids biomedical research.