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

You might also read

Related Articles

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

Sort by
Same author

Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion.

Entropy (Basel, Switzerland)·2023
Same author

Rainwater-Removal Image Conversion Learning with Training Pair Augmentation.

Entropy (Basel, Switzerland)·2023
Same author

Combined Deep Learning Techniques for Mandibular Fracture Diagnosis Assistance.

Life (Basel, Switzerland)·2022
Same author

H<sub>2</sub>O<sub>2</sub>-Responsive amphiphilic polymer with aggregation-induced emission (AIE) for DOX delivery and tumor therapy.

Bioorganic chemistry·2021
Same author

Automatic Detection of Mandibular Fractures in Panoramic Radiographs Using Deep Learning.

Diagnostics (Basel, Switzerland)·2021
Same author

Dihydropyridine-derived BODIPY probe for detecting exogenous and endogenous nitric oxide in mitochondria.

Talanta·2017

Related Experiment Video

Updated: Jan 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K

Image Sand-Dust Removal Using Reinforced Multiscale Image Pair Training.

Dong-Min Son1, Jun-Ru Huang1, Sung-Hak Lee1

  • 1School of Electronic and Electrical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary

This study introduces an advanced image enhancement technique for outdoor surveillance systems, improving visibility and color accuracy during sandstorms. The method uses deep learning and Retinex-based processing to restore clear, sharp images, outperforming existing dust removal solutions.

Keywords:
CycleGANRetinexcolor preservationmulti-scale image

More Related Videos

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
09:00

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

13.7K
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

3.2K

Related Experiment Videos

Last Updated: Jan 15, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.6K
Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography
09:00

Visualization of Failure and the Associated Grain-Scale Mechanical Behavior of Granular Soils under Shear using Synchrotron X-Ray Micro-Tomography

Published on: September 29, 2019

13.7K
Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
06:20

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

Published on: December 6, 2024

3.2K

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Outdoor surveillance systems face challenges with low visibility and color distortion during sandstorms.
  • Conventional image enhancement methods struggle to restore fine details and sharp object boundaries in dusty environments.

Purpose of the Study:

  • To develop an image enhancement method that effectively restores image quality and preserves color consistency under sandstorm conditions.
  • To improve the performance of outdoor surveillance systems by mitigating the effects of sand-dust interference.

Main Methods:

  • A Cycle-Consistent Generative Adversarial Network (CycleGAN) was trained with unpaired images to generate synthetic data.
  • CycleGAN was retrained using generated and clear images for dust-interfered image transformation.
  • Retinex-based processing and selection of A and B chrominance channels were incorporated for enhanced clarity and color preservation.

Main Results:

  • The proposed method effectively restored image color and removed sand-dust interference, significantly enhancing visual quality.
  • Achieved a superior Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score of 17.238 and a Local Phase Coherence-Sharpness Index (LPC-SI) of 0.973.
  • Outperformed established dust removal algorithms (SDIE, CVCGCBD, ROP+) and machine learning methods (Fusion, TOENet).

Conclusions:

  • The developed image enhancement technique significantly improves visual clarity in sandstorm conditions.
  • The method is highly applicable to Closed-Circuit Television (CCTV) systems, enhancing practical surveillance capabilities.
  • The approach offers a robust solution for maintaining image quality in adverse weather conditions.