Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

8.0K
Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
8.0K
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.0K
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...
7.0K

You might also read

Related Articles

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

Sort by
Same author

A Two-Stage, Semi-Supervised Deep Learning Framework for the Detection and Classification of Ambient Pollen using Evanescent Wave Scattering.

Environmental science & technology·2026
Same author

Designing a Multimodal Microscopic Device for Label-Free Detection of Squamous Cell Carcinoma.

Journal of biophotonics·2025
Same author

Compact Linnik-type hyperspectral quantitative phase microscope for advanced classification of cellular components.

Journal of biophotonics·2024
Same author

Multispectral polarization microscopy of different stages of human oral tissue: A polarization study.

Journal of biophotonics·2023
Same author

SERS Nanowire Chip and Machine Learning-Enabled Classification of Wild-Type and Antibiotic-Resistant Bacteria at Species and Strain Levels.

ACS applied materials & interfaces·2023
Same author

Point-of-care devices based on fluorescence imaging and spectroscopy for tumor margin detection during breast cancer surgery: Towards breast conservation treatment.

Lasers in surgery and medicine·2023

Related Experiment Video

Updated: Jun 29, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.5K

High-resolution cell imaging using white light phase shifting interferometry and iterative phase deconvolution.

Shubham Tiwari1,2, Shilpa Tayal2, Shivam Trivedi2

  • 1SeNSE, Indian Institute of Technology Delhi, New Delhi, India.

Journal of Biophotonics
|April 3, 2024
PubMed
Summary

A new optimization algorithm enhances quantitative phase imaging (QPI) resolution and accuracy. This method reveals sub-cellular structures in bacteria, offering valuable biological insights.

Keywords:
cell imagingconstrained optimizationdeconvolutionphase shifting interferometryquantitative phase imagingwhite light

More Related Videos

Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

11.8K
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K

Related Experiment Videos

Last Updated: Jun 29, 2025

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.5K
Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

11.8K
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

15.6K

Area of Science:

  • Optics and Photonics
  • Biophysics
  • Computational Imaging

Background:

  • Quantitative Phase Imaging (QPI) is crucial for label-free cell visualization.
  • Existing QPI methods often face limitations in resolution and contrast.
  • Deconvolution algorithms are essential for improving image quality in QPI.

Purpose of the Study:

  • To develop and validate an optimization algorithm for deconvolution in QPI.
  • To enhance the resolution and accuracy of phase map recovery.
  • To demonstrate the algorithm's effectiveness on biological samples.

Main Methods:

  • A constrained optimization problem using a complex gradient operator for deconvolution.
  • Application of the algorithm to white light-based phase shifting interferometry (WLPSI).
  • Testing on both simulated and real-world objects, including Escherichia coli.

Main Results:

  • Significant improvements in resolution and contrast were observed.
  • Sub-cellular structures in Escherichia coli, previously invisible, were resolved.
  • The algorithm successfully recovered high-resolution phase maps.

Conclusions:

  • The presented optimization algorithm effectively enhances QPI performance.
  • It enables visualization of fine biological structures, aiding cell function studies.
  • The algorithm is simple to implement and adaptable to various QPI techniques.