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

An Algorithm for Identifying Unsafe Behaviors of Miners Based on the Improved AlphaPose.

Sensors (Basel, Switzerland)·2026
Same author

Blood Vessel Segmentation of Fundus Retinal Images Based on Improved Frangi and Mathematical Morphology.

Computational and mathematical methods in medicine·2021
Same author

Quantitative proteomics identifies surfactant-resistant alpha-synuclein in cerebral cortex of Parkinsonism-dementia complex of Guam but not Alzheimer's disease or progressive supranuclear palsy.

The American journal of pathology·2007
Same author

Different supramolecular assemblies in two 1:1 proton-transfer compounds of sulfobenzoic acids with aromatic amines.

Acta crystallographica. Section C, Crystal structure communications·2007
Same author

Identification of proteins involved in microglial endocytosis of alpha-synuclein.

Journal of proteome research·2007
Same author

Biomarkers for Alzheimer's disease.

Expert review of neurotherapeutics·2007

Related Experiment Video

Updated: Jun 23, 2025

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

2.7K

An Image Dehazing Algorithm for Underground Coal Mines Based on gUNet.

Feng Tian1,2, Lishuo Gao1, Jing Zhang3

  • 1College of Communication and Information Technology, Xi'an University of Science and Technology, Xi'an 710600, China.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

A new image dehazing algorithm, CCDF-gUNet, effectively processes underground coal mine images. It enhances detail and color accuracy, overcoming limitations of existing methods for clearer surveillance footage.

Keywords:
CADSConvU-Netfusion loss functionimage dehazingresidual attention convolutionunderground coal mines

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

13.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

Related Experiment Videos

Last Updated: Jun 23, 2025

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

2.7K
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

13.4K
Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer
07:53

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer

Published on: October 13, 2023

1.4K

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Existing image dehazing algorithms struggle with underground coal mine imagery, leading to incomplete haze removal, color distortion, and loss of critical detail.
  • These limitations hinder the effectiveness of visual analysis and surveillance in challenging mining environments.

Purpose of the Study:

  • To develop an advanced image dehazing algorithm specifically designed for the unique challenges of underground coal mine environments.
  • To improve feature extraction, detail preservation, and color fidelity in dehazed images.

Main Methods:

  • Introduction of Dynamic Snake Convolution (DSConv) for enhanced feature extraction.
  • Integration of residual attention convolution blocks for simultaneous local and global information processing.
  • Utilization of the Coordinate Attention (CA) module to capture key feature information.
  • Implementation of a fusion loss function to maintain image detail and structural consistency.

Main Results:

  • The proposed CCDF-gUNet algorithm achieved a PSNR of 30.72 dB and SSIM of 0.976 on the Haze-4K dataset.
  • On a self-made dataset, the algorithm yielded a PSNR of 31.18 dB and SSIM of 0.971.
  • Demonstrated superior performance in dehazing, color accuracy, and detail retention compared to existing methods.

Conclusions:

  • The CCDF-gUNet algorithm effectively addresses the limitations of current dehazing techniques in underground coal mine settings.
  • The method successfully removes haze, prevents color distortion, and preserves crucial image details and edge information.
  • Provides a valuable reference for advancing image processing in coal mine surveillance and related applications.