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

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

5.3K
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...
5.3K
X-ray Imaging01:24

X-ray Imaging

7.2K
German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
7.2K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

909
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
909
Computed Tomography01:10

Computed Tomography

6.2K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
6.2K
Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

9.5K
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...
9.5K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.5K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.5K

You might also read

Related Articles

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

Sort by
Same author

Clinical significance of high-density lipoprotein cholesterol and its dynamic change in patients with lymphoma-associated hemophagocytic lymphohistiocytosis.

Therapeutic advances in medical oncology·2026
Same author

Soil organic carbon loss driven by water erosion in northeast China's black soil region (1991-2019).

Journal of environmental management·2026
Same author

Lighting effects on optimal facial regions for remote heart rate measurement.

NPJ cardiovascular health·2026
Same author

Image Restoration Learning via Noisy Supervision in Fourier Domain.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

EventTracer: Fast Path Tracing-Based Event Stream Rendering.

IEEE transactions on visualization and computer graphics·2026
Same author

Seasonal Divergence between Microbiomes on Microplastics and Natural Particles Increases with Rising Water Temperatures in Urban Rivers.

Environmental science & technology·2026

Related Experiment Video

Updated: Sep 11, 2025

Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
10:28

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

Published on: July 5, 2016

10.4K

Robust holographic imaging for real-world applications with joint optimization.

Yunping Zhang, Edmund Y Lam

    Optics Express
    |August 13, 2025
    PubMed
    Summary

    This study introduces J-Net, a neural network for digital inline holography. J-Net reconstructs images without needing to know the exact distance, improving practical holographic imaging applications.

    Area of Science:

    • Optics and Photonics
    • Computational Imaging
    • Artificial Intelligence in Science

    Background:

    • Digital inline holography provides lensless imaging but requires precise alignment and calibration.
    • Propagation distance is a critical parameter that is difficult to control in real-world holographic systems.

    Purpose of the Study:

    • To develop a robust method for holographic reconstruction that does not require prior knowledge of the propagation distance.
    • To mitigate the limitations of traditional digital inline holography systems.

    Main Methods:

    • Introduction of J-Net, an untrained neural network.
    • Simultaneous reconstruction of complex-valued object magnitude and propagation distance from a single hologram.
    • Experimental validation of J-Net's performance.

    More Related Videos

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.4K
    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
    09:04

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

    Published on: January 14, 2020

    9.8K

    Related Experiment Videos

    Last Updated: Sep 11, 2025

    Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
    10:28

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

    Published on: July 5, 2016

    10.4K
    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.4K
    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
    09:04

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

    Published on: January 14, 2020

    9.8K

    Main Results:

    • J-Net successfully reconstructs high-quality amplitude and phase images.
    • The network demonstrates robustness to variations and mismatches in propagation distance.
    • No prior system calibration or knowledge of propagation distance is needed.

    Conclusions:

    • J-Net offers a practical solution for robust holographic imaging.
    • The method has the potential to enable widespread adoption of holographic techniques in various fields.
    • Untrained neural networks can effectively address challenges in optical metrology.