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

Perceptual Constancy01:12

Perceptual Constancy

1.2K
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
1.2K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

8.0K
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...
8.0K
Color Vision01:24

Color Vision

1.3K
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
1.3K
Deconvolution01:20

Deconvolution

520
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...
520
Focusing of Light in the Eye01:16

Focusing of Light in the Eye

5.2K
Light rays enter the eye through the cornea, a transparent dome-shaped tissue that is the eye's outermost layer. The cornea bends or refracts, light rays traveling to the pupil. The shape of the cornea determines how much of the light is bent and whether the image will be focused correctly on the retina at the back of the eye. Once the light has passed through both refraction layers, it converges into a single focal point onto a small area. This is where photoreceptors start transforming...
5.2K

You might also read

Related Articles

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

Sort by
Same author

ARetinex-Net: Low-light image enhancement with adaptive retinex model and global illumination representation.

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

PDE4A knockdown rescues alcohol-induced cognitive impairment and synaptic dysfunction via the cAMP/PKA/CREB pathway.

The international journal of neuropsychopharmacology·2026
Same author

Targeted Degradation of STING by a Neutrophil Membrane-Coated Nanoplatform Suppresses Microglial Pyroptosis After Subarachnoid Hemorrhage.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

LoopExpose: An Unsupervised Framework for Arbitrary-Length Exposure Correction.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

EZH2 Regulates the Pluripotency of Mouse Embryonic Stem Cells by Modulating <i>Nanog</i> Expression Under PKC Inhibition.

Biology·2026
Same author

Dihydromyricetin attenuates platelet hyperactivity in HFD/STZ-induced diabetic mice by inhibiting intraplatelet ROS generation.

Food & function·2026

Related Experiment Video

Updated: Jan 8, 2026

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
05:58

Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

Published on: August 29, 2018

9.2K

Cross-Frequency Attention and Color Contrast Constraint for Remote Sensing Dehazing.

Yuxin Feng, Jufeng Li, Tao Huang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |December 22, 2025
    PubMed
    Summary

    This study introduces a novel deep learning method for remote sensing image dehazing, improving texture detail and color accuracy. The approach enhances high-frequency information modeling and introduces a color contrast loss for better visual results.

    More Related Videos

    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

    2.8K
    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    10.3K

    Related Experiment Videos

    Last Updated: Jan 8, 2026

    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking
    05:58

    Using Rapid Serial Visual Presentation to Measure Set-Specific Capture, a Consequence of Distraction While Multitasking

    Published on: August 29, 2018

    9.2K
    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

    2.8K
    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
    13:00

    Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

    Published on: January 23, 2017

    10.3K

    Area of Science:

    • Computer Vision
    • Remote Sensing
    • Deep Learning

    Background:

    • Deep learning methods for remote sensing image dehazing face challenges in preserving fine texture details and accurate colors.
    • Insufficient modeling of high-frequency information and lack of effective color restoration constraints are key limitations.

    Purpose of the Study:

    • To develop an advanced deep learning model for remote sensing image dehazing.
    • To simultaneously improve texture detail restoration and color accuracy in dehazed images.

    Main Methods:

    • Developed an omni-directional high-frequency feature extraction mechanism using wavelet transform for multi-directional components.
    • Designed a high-frequency prompt attention module for enhanced local high-frequency representation and edge sharpness.
    • Proposed a color contrast loss function in the HSV color space for improved color restoration.

    Main Results:

    • The proposed method significantly enhances the model's capability in edge sharpness restoration and texture detail reconstruction.
    • Experimental results demonstrate superior performance over existing methods in both texture detail restoration and color consistency.
    • The color contrast loss guides the model to generate dehazed images with consistent colors and natural appearance.

    Conclusions:

    • The novel approach effectively addresses limitations in current deep learning-based remote sensing image dehazing.
    • The method achieves state-of-the-art results in preserving details and restoring accurate colors.
    • The developed techniques offer a promising direction for future research in image dehazing.