Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Technical Note: Experimental characterization of the dose deposition in parallel MRI-linacs at various magnetic field strengths.

Medical physics·2019

Same author

Comprehensive immune characterization and T-cell receptor repertoire heterogeneity of retroperitoneal liposarcoma.

Cancer science·2019

Same author

Fibroin/peptide co-functionalized calcium titanate nanorods improve osteoinductivity of titanium via mimicking osteogenic niche.

Materials science & engineering. C, Materials for biological applications·2019

Same author

Association of School Residential PM<sub>2.5</sub> with Childhood High Blood Pressure: Results from an Observational Study in 6 Cities in China.

International journal of environmental research and public health·2019

Same author

Role of tri-ponderal mass index in cardio-metabolic risk assessment in children and adolescents: compared with body mass index.

International journal of obesity (2005)·2019

Same author

Effect of Overweight and Obesity on High Blood Pressure in Chinese Children and Adolescents.

Obesity (Silver Spring, Md.)·2019

Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026

Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026

Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026

Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026

Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026

Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 25, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

Stitching sub-aperture in digital holography based on machine learning.

Feng Pan, Bin Dong, Wen Xiao

|April 1, 2020

Summary

This summary is machine-generated.

Machine learning simplifies sub-aperture stitching in digital holography (DH). This novel approach improves spatial resolution and measurement accuracy for complex optical surfaces, making optical metrology more reliable.

More Related Videos

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Published on: July 5, 2016

Related Experiment Videos

Last Updated: Dec 25, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization

Published on: July 5, 2016

Area of Science:

Optical Metrology
Digital Holography
Machine Learning Applications

Background:

Sub-aperture stitching in digital holography (DH) is crucial for enhancing spatial resolution and measuring large optical surfaces.
Current stitching methods can be complex and cumbersome, especially for intricate geometries.

Purpose of the Study:

To introduce a novel machine learning-based approach for sub-aperture stitching in digital holography.
To demonstrate the potential of machine learning in simplifying and improving the accuracy of optical metrology processes.

Main Methods:

A machine learning network was constructed based on the computational model of sub-aperture stitching.
The network was trained using an array of phase-map sub-apertures from an off-axis digital holographic system.
Alignment errors and system aberrations in sub-aperture maps were corrected through network training.

Main Results:

The proposed machine learning approach successfully corrected alignment errors and system aberrations.
Accurate measurement of hemisphere surface topography was achieved, validating the method.
The study demonstrated the effectiveness of machine learning in sub-aperture stitching.

Conclusions:

Machine learning offers a powerful tool to simplify complex sub-aperture stitching processes in digital holography.
The developed approach enhances the reliability and accuracy of optical metrology for large and complex optical surfaces.