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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

2.7K
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.
2.7K

You might also read

Related Articles

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

Sort by
Same author

Noise and defocus trade-off in depth of field extension with a co-designed binary phase mask.

Applied optics·2026
Same author

Abel inversion from central-projection background oriented schlieren observations for reconstruction of axisymmetric refractive media.

Applied optics·2025
Same author

Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging.

Light, science & applications·2024
Same author

Embedded Processing for Extended Depth of Field Imaging Systems: From Infinite Impulse Response Wiener Filter to Learned Deconvolution.

Sensors (Basel, Switzerland)·2023
Same author

Regressing Image Sub-Population Distributions with Deep Learning.

Sensors (Basel, Switzerland)·2022
Same author

Low-resolution description of the conformational space for intrinsically disordered proteins.

Scientific reports·2022

Related Experiment Video

Updated: May 6, 2026

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
11:34

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

16.3K

Passive depth estimation using chromatic aberration and a depth from defocus approach.

Pauline Trouvé, Frédéric Champagnat, Guy Le Besnerais

    Applied Optics
    |November 13, 2013
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel passive depth estimation method using a chromatic camera and a specialized depth from defocus algorithm. This approach leverages spectrally varying blurs for more accurate depth extraction in color images.

    More Related Videos

    High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon
    08:18

    High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

    Published on: June 16, 2020

    8.1K
    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    15.7K

    Related Experiment Videos

    Last Updated: May 6, 2026

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.3K
    High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon
    08:18

    High-Accuracy Correction of 3D Chromatic Shifts in the Age of Super-Resolution Biological Imaging Using Chromagnon

    Published on: June 16, 2020

    8.1K
    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    15.7K

    Area of Science:

    • Computer Vision
    • Optical Engineering
    • Image Processing

    Background:

    • Passive depth estimation is crucial for various applications.
    • Existing depth from defocus (DFD) methods often struggle with color images due to spectrally varying blurs.
    • Chromatic aberration in lenses offers a unique property for depth perception.

    Purpose of the Study:

    • To propose a novel passive depth estimation method.
    • To develop an original depth from defocus (DFD) algorithm tailored for color images with spectrally varying defocus blurs.
    • To experimentally evaluate the proposed method using a prototype chromatic camera.

    Main Methods:

    • Utilizing a camera with longitudinal chromatic aberration and an RGB sensor to capture spectrally variable in-focus planes.
    • Developing a new DFD algorithm specifically designed for color images exhibiting spectrally varying defocus blurs.
    • Designing and building a prototype chromatic camera for experimental validation.

    Main Results:

    • The proposed method demonstrates effective passive depth estimation.
    • The chromatic camera combined with the novel DFD algorithm successfully extracts depth information from color images.
    • Experimental results show comparable or improved performance against active ranging sensors.

    Conclusions:

    • The integration of a chromatic lens and a specialized DFD algorithm provides an effective approach for passive depth estimation.
    • This method offers a promising alternative to active ranging sensors for depth perception in various scenes.
    • The developed chromatic camera prototype validates the practical applicability of the proposed technique.