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

Vision01:24

Vision

52.9K
Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
52.9K
Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

4.3K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
4.3K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

508
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.
508

You might also read

Related Articles

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

Sort by
Same author

Development and validation of an interpretable model based on ultrasound radiomics for predicting Ki-67 expression levels in breast cancer.

Translational cancer research·2026
Same author

Seasonal shifts in mercury speciation and relative methylation potential in cold-arid seasonally ice-covered lakes.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Sex-Specific Adaptive Strategies of <i>Populus euphratica</i> Along Developmental and Canopy Gradients Based on Leaf Trait Networks.

Plants (Basel, Switzerland)·2026
Same author

Spatial Distribution, Risk Assessment, and Source Apportionment of Heavy Metals in Soils from the Sorghum Cultivation Base in the Chishui River Basin, China.

Toxics·2026
Same author

Global and Local Visual-Textual Alignment for Open Vocabulary Object Detection.

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

A machine-learning-based reconstruction of surface mass balance over the Greenland Ice Sheet from 1950 to 2020.

Scientific data·2026

Related Experiment Video

Updated: May 24, 2025

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

12.2K

Diffusion Models in Low-Level Vision: A Survey.

Chunming He, Yuqi Shen, Chengyu Fang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
    PubMed
    Summary

    This paper reviews denoising diffusion models for low-level vision tasks, offering a comprehensive overview of their theory, applications, and future directions. It synthesizes advances in diffusion models for high-quality image generation and processing.

    More Related Videos

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    8.9K
    From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope
    15:10

    From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

    Published on: October 9, 2014

    11.4K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
    13:26

    Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

    Published on: August 11, 2016

    12.2K
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    8.9K
    From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope
    15:10

    From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope

    Published on: October 9, 2014

    11.4K

    Area of Science:

    • Computer Vision
    • Deep Learning
    • Generative Models

    Background:

    • Deep generative models excel in low-level vision.
    • Diffusion models are prominent for high-quality image generation.
    • A comprehensive survey of diffusion models in low-level vision is lacking.

    Purpose of the Study:

    • To provide the first comprehensive review of denoising diffusion models in low-level vision.
    • To cover theoretical and practical contributions.
    • To synthesize advances and identify future research directions.

    Main Methods:

    • Outlined three general diffusion modeling frameworks.
    • Explored connections with other deep generative models.
    • Categorized diffusion models by framework and application.
    • Reviewed benchmarks and evaluation metrics.
    • Evaluated diffusion models across six representative tasks.

    Main Results:

    • Diffusion models demonstrate powerful generative capabilities in low-level vision.
    • Applications span natural image processing, medical imaging, remote sensing, and video processing.
    • Extensive evaluation shows quantitative and qualitative performance.

    Conclusions:

    • Denoising diffusion models are crucial for low-level vision tasks.
    • Current limitations exist, with promising future research directions identified.
    • This review fosters a deeper understanding of diffusion models in the field.