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

Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

393
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
393

You might also read

Related Articles

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

Sort by
Same author

Laboratory Production of Biofuels and Biochemicals from a Rapeseed Oil through Catalytic Cracking Conversion.

Journal of visualized experiments : JoVE·2016
Same author

Derivation of soil-screening thresholds to protect the chisel-toothed kangaroo rat from uranium mine waste in northern Arizona.

Archives of environmental contamination and toxicology·2013
Same author

Transgenic mouse model for monitoring endoplasmic reticulum stress in vivo.

Nature medicine·2004
Same author

Assessing the toxicity and teratogenicity of pond water in north-central Minnesota to amphibians.

Environmental science and pollution research international·2004

Related Experiment Video

Updated: Sep 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K

Reconstructing high fidelity digital rock images using deep convolutional neural networks.

Majid Bizhani1, Omid Haeri Ardakani2,3, Edward Little2

  • 1Natural Resources Canada, Geological Survey of Canada, 3303 33 Street NW, Calgary, AB, T2L 2A7, Canada. majid.bizhani@NRCan-RNCan.gc.ca.

Scientific Reports
|March 12, 2022
PubMed
Summary

Convolutional neural networks (CNNs) accelerate the analysis of geological images by rapidly denoising, deblurring, and enhancing resolution. This AI-driven approach improves the statistical relevance of digital rock analysis from scanning electron microscopy and micro-CT scans.

More Related Videos

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.5K
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

666

Related Experiment Videos

Last Updated: Sep 30, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.4K
Deep Learning-Based Segmentation of Cryo-Electron Tomograms
10:25

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

9.5K
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

666

Area of Science:

  • Geosciences
  • Digital Rock Physics
  • Artificial Intelligence in Imaging

Background:

  • Imaging techniques like Scanning Electron Microscopy (SEM) and micro-CT scanning are crucial for geosciences.
  • Acquiring high-quality 3D digital rock images is time-consuming and prone to artifacts like noise.
  • Image artifacts hinder accurate determination of rock properties.

Purpose of the Study:

  • To apply convolutional neural networks (CNNs) for rapid restoration of digital rock images.
  • To demonstrate the effectiveness of CNNs in denoising, deblurring, and super-resolving geological images.
  • To enable faster imaging of larger rock samples for improved statistical analysis.

Main Methods:

  • Utilized several convolutional neural networks (CNNs) for image processing tasks.
  • Applied CNNs for denoising, deblurring, and super-resolution of SEM and micro-CT scan images.
  • Integrated multiple CNNs in an end-to-end fashion to enhance reconstruction quality.

Main Results:

  • Achieved rapid image denoising without prior knowledge of noise characteristics.
  • Demonstrated successful deblurring and super-resolution of digital rock images.
  • Showcased simultaneous denoising, deblurring, and super-resolution using chained CNNs.

Conclusions:

  • CNNs offer a powerful tool for efficient and high-quality restoration of scientific images in geosciences.
  • The proposed CNN approach significantly reduces processing time for digital rock analysis.
  • This method enhances the statistical relevance and reliability of geoscientific imaging studies.