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

1.0K
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.
1.0K
Light Acquisition02:16

Light Acquisition

8.6K
In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.
8.6K
Deconvolution01:20

Deconvolution

270
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
270

You might also read

Related Articles

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

Sort by
Same author

Frequency-scanning nonlinearity suppression for FSI ranging based on a phenomenological modeling approach.

Optics express·2026
Same author

Virtual-interferometer approach to frequency-scanning nonlinearity calibration via harmonic extraction from a Fabry-Pérot etalon.

Optics letters·2026
Same author

Magnetic Resonance Imaging or Confirmatory Biopsy for Patients With Prostate Cancer Receiving Active Surveillance.

JAMA oncology·2025
Same author

Automated Extraction of Imaging and Pathology Data From Diverse Prostate Cancer Electronic Records.

JCO clinical cancer informatics·2025
Same author

Deep learning-based automated detection and multiclass classification of soil-transmitted helminths and Schistosoma mansoni eggs in fecal smear images.

Scientific reports·2025
Same author

Adaptive optics in single objective inclined light sheet microscopy enables three-dimensional localization microscopy in adult <i>Drosophila</i> brains.

Frontiers in neuroscience·2022
Same journal

Multi-module collaborative optimization-driven fast speckle correlation imaging in variable environments.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Secrecy performance analysis of NOMA-UWOC systems over a vertically stratified WGG oceanic turbulence channel.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Backscattering of plane waves in a composite system containing a rough surface and anisotropic scatterers.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Aspherical surface construction methods based on extended Jacobi polynomials.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

OCT sidelobe suppression method based on dual-path phase sinusoidal modulation and minimum value fusion.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
Same journal

Optical design concepts using wavelength-selective diffractive optics to enable miniaturized multimodal endoscopic imaging across separated spectral ranges.

Journal of the Optical Society of America. A, Optics, image science, and vision·2026
See all related articles

Related Experiment Video

Updated: Sep 25, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.0K

Extended scene deep learning wavefront sensing.

Bas de Bruijne, Gleb Vdovin, Oleg Soloviev

    Journal of the Optical Society of America. A, Optics, Image Science, and Vision
    |April 26, 2022
    PubMed
    Summary
    This summary is machine-generated.

    We enhanced Shack-Hartmann sensor performance by combining blind deconvolution and deep learning. This novel approach improves sensitivity and resolution beyond traditional methods, even with extended objects.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    659
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K

    Related Experiment Videos

    Last Updated: Sep 25, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.0K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    659
    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
    05:41

    A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

    Published on: February 6, 2020

    9.5K

    Area of Science:

    • Optics and Photonics
    • Image Processing
    • Wavefront Sensing

    Background:

    • Shack-Hartmann sensors are crucial for measuring wavefronts.
    • Standard processing relies solely on spot displacement, limiting sensitivity and resolution.
    • Advanced image processing techniques can potentially overcome these limitations.

    Purpose of the Study:

    • To improve the sensitivity and resolution of Shack-Hartmann sensors.
    • To develop a novel processing method for Shack-Hartmann images.
    • To demonstrate the applicability of the method using extended objects.

    Main Methods:

    • Applied a combination of blind deconvolution and deep learning algorithms.
    • Utilized intensity information from spot positions.
    • Analyzed the fine structure of individual lenslet images.

    Main Results:

    • Achieved increased sensor sensitivity and resolution beyond standard processing limits.
    • Successfully processed Shack-Hartmann images using the combined method.
    • Demonstrated wavefront sensing with extended objects as a reference.

    Conclusions:

    • Blind deconvolution and deep learning offer superior Shack-Hartmann image processing.
    • The method enhances sensor performance by leveraging more image information.
    • This technique is applicable for wavefront sensing with extended reference objects.