Jove
Visualize
Contact Us

Related Concept Videos

Deconvolution01:20

Deconvolution

186
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...
186
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.5K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.5K
Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview01:13

Attenuated Total Reflectance (ATR) Infrared Spectroscopy: Overview

415
Attenuated total reflectance (ATR) infrared spectroscopy is a powerful analytical technique used to study the composition of materials. It is widely employed in chemistry, materials science, forensic science, and other fields where sample characterization is required. ATR has several advantages over traditional transmission IR spectroscopy, including the requirement of little to no sample preparation and the ability to analyze a wide range of samples.
The ATR process begins by directing a beam...
415

You might also read

Related Articles

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

Sort by
Same author

Enhanced COMP expression induced by Helicobacter pylori drives gastric cancer progression by NOTCH2/SMAD3 pathway.

Journal of gastroenterology·2026
Same author

Are green spaces healthier in spring? Examining seasonal variations in the association between urban green spaces and emotional well-being in Wuhan, China.

Frontiers in public health·2026
Same author

Neural stem cell transplantation in rodent models of traumatic brain injury: a systematic review and meta-analysis.

Frontiers in bioengineering and biotechnology·2026
Same author

Enhanced ITGA3 expression induced by helicobacter pylori infection facilitates gastric cancer progression via NF-κB and Smad4.

Molecular and cellular biochemistry·2026
Same author

Neuroplastic Mechanisms of Acupuncture in Post-Stroke Motor Recovery: A Randomized Multimodal MRI Trial.

CNS neuroscience & therapeutics·2026
Same author

Nicotine promotes cell proliferation through α5-nAChR/RRM2 axis in lung adenocarcinoma.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026
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 Experiment Video

Updated: Jul 16, 2025

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

568

Remote Sensing Image Dehazing through an Unsupervised Generative Adversarial Network.

Liquan Zhao1, Yanjiang Yin1, Tie Zhong1

  • 1Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin 132012, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary

This study introduces an unsupervised generative adversarial network for remote sensing image dehazing. The novel method effectively removes haze, significantly improving image quality and data interpretation.

Keywords:
attention modulemulti-scale feature-extraction moduleremote sensing image dehazingunsupervised generating adversarial network

More Related Videos

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
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K

Related Experiment Videos

Last Updated: Jul 16, 2025

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

568
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
Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters
07:05

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

Published on: June 18, 2021

2.5K

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Haze significantly degrades remote sensing image quality, hindering information extraction.
  • Existing dehazing methods struggle with the complexities of remote sensing imagery.

Purpose of the Study:

  • To develop an unsupervised generative adversarial network (GAN) for effective remote sensing image dehazing.
  • To improve the visual quality and interpretability of remote sensing data affected by haze.

Main Methods:

  • Proposed an unsupervised GAN with two generators and two discriminators for image dehazing.
  • Incorporated multi-scale feature-extraction and attention modules within an encoder-decoder generator architecture.
  • Introduced an improved loss function with color-constancy loss and designed a multi-scale discriminator.

Main Results:

  • Achieved the highest peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) metrics compared to state-of-the-art methods.
  • Demonstrated superior performance in effectively removing haze from remote sensing images.
  • The proposed attention modules effectively guide feature extraction for haze and texture emphasis.

Conclusions:

  • The unsupervised GAN effectively mitigates haze in remote sensing images.
  • The novel architecture and loss function lead to significant improvements in image quality.
  • The method offers a promising solution for enhancing remote sensing data analysis.