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

Blind Procedures02:07

Blind Procedures

13.1K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.1K
Motion-induced Blindness06:03

Motion-induced Blindness

7.7K
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University
One thing becomes very salient after basic exposure to the science of visual perception and sensation: what people see is a creation of the brain. As a result people may fail to see things, see things that are not there, or see things in a distorted way.
To distinguish between physical reality and what people perceive, scientists use the term awareness to refer to what people perceive. To study awareness, vision scientists...
7.7K
Inattentional Blindness14:51

Inattentional Blindness

14.6K
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University
We generally think that we see things pretty well if they are close by and right in front of us. But do we? We know that visual attention is a property of the human brain that controls what parts of the visual world we process, and how effectively. Limited attention means that we can't process everything at once, it turns out, even things that might be right in front of us.
In the 1960s, the renowned cognitive psychologist...
14.6K
Ranks01:02

Ranks

464
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
464
Blinding01:11

Blinding

3.8K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
3.8K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.4K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Artificial Neural Network-Assisted Facial Analysis for Planning of Orthognathic Surgery.

Journal of clinical and experimental dentistry·2024
Same author

The Cell Tracking Challenge: 10 years of objective benchmarking.

Nature methods·2023
Same author

Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples.

IEEE transactions on neural networks and learning systems·2021
Same author

Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and integration with horizontal thickness profile.

Computers in biology and medicine·2019
Same author

Automated Spatial Pattern Analysis for Identification of Foot Arch Height From 2D Foot Prints.

Frontiers in physiology·2018
Same author

Unsupervised Retinal Vessel Segmentation Using Combined Filters.

PloS one·2016
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

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

Semantic Frame Interpolation.

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

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

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

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Jan 20, 2026

Studying Visual Awareness and Motion-Induced Blindness
06:03

Studying Visual Awareness and Motion-Induced Blindness

Published on: April 30, 2023

7.7K

Burst ranking for blind multi-image deblurring.

Fidel A Guerrero Pena, Pedro D Marrero Fernandez, Tsang Ing Ren

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 4, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an advanced algorithm for multi-image deblurring that automatically selects the best frames from a burst. This method overcomes limitations of current techniques, improving image reconstruction quality in challenging real-world scenarios.

    More Related Videos

    Investigating Visual Awareness and Inattentional Blindness
    14:51

    Investigating Visual Awareness and Inattentional Blindness

    Published on: April 30, 2023

    14.6K
    Blind Procedures: Single-blind and Double-blind Studies
    02:07

    Blind Procedures: Single-blind and Double-blind Studies

    13.1K

    Related Experiment Videos

    Last Updated: Jan 20, 2026

    Studying Visual Awareness and Motion-Induced Blindness
    06:03

    Studying Visual Awareness and Motion-Induced Blindness

    Published on: April 30, 2023

    7.7K
    Investigating Visual Awareness and Inattentional Blindness
    14:51

    Investigating Visual Awareness and Inattentional Blindness

    Published on: April 30, 2023

    14.6K
    Blind Procedures: Single-blind and Double-blind Studies
    02:07

    Blind Procedures: Single-blind and Double-blind Studies

    13.1K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Deep Learning

    Background:

    • Current multi-image deblurring methods struggle with misaligned or irrelevant frames in image bursts.
    • This limitation often leads to suboptimal image reconstructions or necessitates manual frame selection.
    • Automating frame selection is complex due to the vast number of potential image combinations.

    Purpose of the Study:

    • To develop an incremental aggregation algorithm for multi-image deblurring with automatic image selection.
    • To address the challenges posed by misaligned or out-of-context frames in image bursts.
    • To enhance the quality of deblurred images by intelligently fusing selected frames.

    Main Methods:

    • A two-step approach was employed for multi-image deblurring.
    • A deep convolutional neural network was trained to learn an image comparison function for ranking frames.
    • An incremental Fourier burst accumulation with a reconstruction degradation mechanism was utilized for frame fusion.

    Main Results:

    • The proposed algorithm demonstrates superior performance compared to existing multi-image deblurring methods.
    • It effectively handles bursts containing misaligned or irrelevant frames, achieving better reconstructions.
    • Experimental validation on synthetic and real datasets confirms the algorithm's effectiveness.

    Conclusions:

    • The developed algorithm offers an effective solution for multi-image deblurring, particularly in challenging scenarios.
    • Automatic frame selection significantly improves reconstruction quality without manual intervention.
    • The method provides a robust and efficient approach to deblurring image bursts.