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

Deconvolution01:20

Deconvolution

202
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...
202
Blind Procedures02:07

Blind Procedures

10.7K
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...
10.7K
Convolution Properties II01:17

Convolution Properties II

240
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
240
Convolution Properties I01:20

Convolution Properties I

191
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
191
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

753
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.
753
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K

You might also read

Related Articles

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

Sort by
Same author

V-Sparse: From temporal-spatial visual semantic compression to coarse-to-fine interaction for text-video retrieval.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

<i>Streptococcus suis</i> Stk1 sensitizes epithelial cells to ferroptosis and exacerbates disruption of the respiratory epithelial barrier.

Emerging microbes & infections·2026
Same author

CK2 derived from brain microvascular endothelial cells induces astrocyte inflammatory response in Escherichia coli-induced meningitis.

PLoS pathogens·2025
Same author

Robust structured light with efficient redundant codes.

Optics express·2024
Same author

Learning to Recover Spectral Reflectance From RGB Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2024
Same author

<i>Streptococcus pneumoniae</i> extracellular vesicles aggravate alveolar epithelial barrier disruption via autophagic degradation of OCLN (occludin).

Autophagy·2024
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jul 27, 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

2.8K

Blind Image Deconvolution Using Variational Deep Image Prior.

Dong Huo, Abbas Masoumzadeh, Rafsanjany Kushol

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 8, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Variational Deep Image Prior (VDIP) for blind image deconvolution. VDIP enhances image restoration by combining deep image priors with traditional methods, improving generalization for unseen blurs.

    More Related Videos

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.6K
    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    34.0K

    Related Experiment Videos

    Last Updated: Jul 27, 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

    2.8K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.6K
    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
    13:01

    Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

    Published on: April 10, 2016

    34.0K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Traditional deconvolution methods rely on hand-crafted image priors.
    • Deep learning models offer end-to-end training but lack generalization to novel blurs.
    • Deep Image Prior (DIP) uses network architecture as a prior but faces challenges in architecture selection.

    Purpose of the Study:

    • To propose a novel Variational Deep Image Prior (VDIP) method for blind image deconvolution.
    • To enhance image restoration by integrating additive hand-crafted priors with deep priors.
    • To improve the generalization and constraint capabilities for deconvolution optimization.

    Main Methods:

    • Developed a variational framework for blind image deconvolution.
    • Incorporated additive hand-crafted image priors into the deep prior optimization process.
    • Approximated pixel distributions to mitigate suboptimal solutions in deconvolution.

    Main Results:

    • Mathematical analysis indicates improved optimization constraints with VDIP.
    • Experimental results show superior image quality compared to original DIP on benchmark datasets.
    • Demonstrated enhanced generalization capabilities for deconvolution tasks.

    Conclusions:

    • VDIP offers a more robust approach to blind image deconvolution than standard DIP.
    • The integration of traditional and deep priors effectively addresses limitations in current methods.
    • VDIP shows significant potential for improving image restoration in diverse applications.