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

Phase Contrast and Differential Interference Contrast Microscopy

Phase Contrast and Differential Interference Contrast Microscopy

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...

You might also read

Related Articles

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

Sort by

Same author

Generative Artificial Intelligence for Orthognathic Planning: Patient-Specific 3-Dimensional Jaw Reference Forms Conditioned on the Cranial Base.

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2026

Same author

Eutrophication drives taxonomic and functional trajectories in plastic-associated biofilms.

Water research·2026

Same author

The co-repressor protein HOS15 negatively regulates ethylene signaling via degradation of EIN3 during arabidopsis apical hook formation.

Journal of plant physiology·2026

Same author

Cancer-associated fibroblasts are associated with CD8+ T cell depletion and poor prognosis in colorectal adenocarcinoma: a multi-omics and machine learning analysis.

The journal of pathology. Clinical research·2026

Same author

Combined therapy with dutasteride and tadalafil vs dutasteride or tadalafil monotherapy in benign prostatic hyperplasia: a randomised phase III trial.

BJU international·2026

Same author

Optimization of THP-1-CAR monocytes utilizing CD32a signaling phagocytosis for antigen-specific T cell activation.

Scientific reports·2026

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 25, 2025

Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

Published on: October 11, 2016

Phase retrieval based on deep learning in grating interferometer.

Ohsung Oh¹, Youngju Kim^2,3, Daeseung Kim¹

¹School of Mechanical Engineering, Pusan National University, Busan, 46241, Republic of Korea.

Scientific Reports

|April 26, 2022

Summary

This summary is machine-generated.

This study introduces a deep learning method using Noise2Noise (N2N) to improve phase contrast imaging in grating interferometry. The technique effectively suppresses artifacts in differential phase contrast images, enhancing image quality for phase stepping applications.

More Related Videos

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

Related Experiment Videos

Last Updated: Sep 25, 2025

Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

Measurement of X-ray Beam Coherence along Multiple Directions Using 2-D Checkerboard Phase Grating

Published on: October 11, 2016

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Shaping the Amplitude and Phase of Laser Beams by Using a Phase-only Spatial Light Modulator

Published on: January 28, 2019

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

Area of Science:

Optics and Photonics
Medical Imaging
Artificial Intelligence

Background:

Grating interferometry enables differential phase contrast (DPCI) imaging with low-coherence sources.
Phase retrieval from DPCI is challenging due to noise and artifacts, limiting applications.
Conventional deep learning denoising needs clean/noisy image pairs, which are difficult to acquire for grating interferometry.

Purpose of the Study:

To implement a deep learning-based phase retrieval method for artifact suppression in grating interferometry.
To adapt the Noise2Noise (N2N) neural network for training with noise/noise image pairs, overcoming the lack of clean reference images.
To enhance the quality of phase contrast images obtained via phase stepping methods.

Main Methods:

A deep learning approach utilizing the Noise2Noise (N2N) neural network was implemented.
Differential phase contrast images (DPCIs) were generated by combining phase stepping images.
These DPCIs served as input/target pairs for training the N2N network.

Main Results:

The N2N network successfully suppressed phase retrieval artifacts in both simulated and measured DPCI data.
Phase contrast images were retrieved with significantly improved quality and reduced artifacts.
The method demonstrated effective artifact reduction in grating interferometer applications.

Conclusions:

Deep learning, specifically the N2N architecture, offers a robust solution for artifact suppression in grating-based phase contrast imaging.
The Noise2Noise approach enables effective training without requiring clean reference images, making it suitable for experimental setups like grating interferometers.
This technique enhances the reliability and quality of phase contrast imaging, broadening its applicability in scientific and medical fields.