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Related Experiment Video

Updated: Oct 17, 2025

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Automating a Dehazing System by Self-Calibrating on Haze Conditions.

Dat Ngo¹, Seungmin Lee¹, Gi-Dong Lee¹

¹Department of Electronics Engineering, Dong-A University, Busan 49315, Korea.

Sensors (Basel, Switzerland)

|October 13, 2021

Summary

This summary is machine-generated.

This study introduces an automated image dehazing system that works on any image, hazy or not. It accurately estimates haze density to blend results and restore scene radiance, outperforming existing methods.

Keywords:

automation dehazing haze density image blending multiscale fusion self-calibration

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Existing image dehazing algorithms often fail on haze-free images, producing artifacts.
Current methods lack automatic haze density detection for arbitrary images.

Purpose of the Study:

To develop an automated dehazing system for diverse image conditions.
To enable robust dehazing regardless of the presence or density of atmospheric haze.

Main Methods:

Simultaneous multiscale fusion-based dehazing and haze density estimation.
Image blending guided by estimated haze density.
Tone remapping for scene radiance restoration.

Main Results:

The proposed system achieves satisfactory dehazing results on both hazy and haze-free images.
Demonstrated superiority over state-of-the-art benchmark dehazing methods.
Self-calibration capability for varying haze conditions.

Conclusions:

The automated system effectively handles arbitrary images by integrating haze density estimation.
The novel approach overcomes limitations of traditional two-stage dehazing techniques.
This method offers a robust solution for image restoration in diverse atmospheric conditions.