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Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Related Experiment Video

Updated: Jul 14, 2025

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Real-time, deep-learning aided lensless microscope.

Jimin Wu¹, Vivek Boominathan², Ashok Veeraraghavan^2,3

¹Department of Bioengineering, Rice University, Houston, Texas 77005, USA.

Biomedical Optics Express

|October 6, 2023

Summary

This summary is machine-generated.

This study introduces a novel lensless microscope with real-time image reconstruction. Utilizing a neural network, it achieves rapid visualization, overcoming limitations of traditional fluorescence microscopes for biological imaging.

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Last Updated: Jul 14, 2025

Lensless Fluorescent Microscopy on a Chip

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Published on: August 17, 2011

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Published on: August 16, 2012

Area of Science:

Biophotonics
Microscopy
Computational Imaging

Background:

Traditional miniaturized fluorescence microscopes face trade-offs between spatial resolution and field of view (FOV).
Lensless microscopes offer a solution but are hindered by slow image reconstruction, preventing real-time visualization.
Close-distance imaging in lensless systems necessitates complex shift-varying deconvolution.

Purpose of the Study:

To develop a lensless microscope capable of real-time image reconstruction.
To overcome the speed limitations of iterative reconstruction algorithms in lensless microscopy.
To enhance the usability of lensless microscopes through rapid visualization.

Main Methods:

Implemented a neural network-based image reconstruction method.
Eliminated the need for iterative reconstruction algorithms.
Developed a lensless microscope system for biological sample imaging.

Main Results:

Achieved over a 10,000-fold increase in reconstruction speed compared to iterative methods.
Enabled real-time visualization at over 25 frames per second (fps).
Obtained a spatial resolution better than 7 µm over a 10 mm² FOV.

Conclusions:

The neural network approach significantly accelerates lensless microscope image reconstruction.
Real-time visualization enhances user interaction, making lensless microscopes more accessible.
This advancement bridges the gap between lensless and conventional microscope usability.