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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...

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Related Experiment Video

Updated: Feb 20, 2026

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

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Non-sky polarization-based dehazing algorithm for non-specular objects using polarization difference and global scene

Yufu Qu, Zhaofan Zou

|October 19, 2017

Summary

This summary is machine-generated.

This study introduces a novel polarization-based image dehazing algorithm that removes haze without needing a sky area. The method enhances visibility in foggy images, outperforming existing techniques in quality assessments.

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Scattering And Absorption of Light in Planetary Regoliths

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Scattering And Absorption of Light in Planetary Regoliths

Scattering And Absorption of Light in Planetary Regoliths

Published on: July 1, 2019

Area of Science:

Computer Vision
Image Processing
Optics

Background:

Hazy images suffer from poor visibility due to light scattering and attenuation by atmospheric particles.
Existing polarization-based dehazing methods often require a sky area, limiting their applicability and parameter selection.
Current methods can also result in high noise levels in the restored images.

Purpose of the Study:

To develop a robust polarization-based image dehazing algorithm independent of sky area detection.
To improve image quality and reduce noise in dehazed images.
To provide a more versatile and less complex dehazing solution.

Main Methods:

Collection of three polarized images using a linear polarizer.
Calculation of maximum and minimum intensity images, assuming negligible object polarization.
Determination of atmospheric light using polarization difference and calculation of airlight's degree of polarization (DPA) by optimizing a correlation equation.
Application of a combined polarization difference and block-matching and 3D (BM3D) filtering for denoising.

Main Results:

The proposed algorithm successfully dehazed images without relying on sky area detection.
The method demonstrated superiority over existing algorithms (Tarel, Fattal, Ren, Berman) in no-reference quality assessments (NRQA, BRISQUE, AQI).
The dehazing process was effective even without complex model assumptions.

Conclusions:

The non-sky polarization-based dehazing algorithm offers a significant advancement in image restoration for foggy conditions.
The approach is versatile, applicable to images with or without sky areas, and yields high-quality, less noisy results.
This method provides a more reliable and efficient solution for image dehazing compared to previous techniques.