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Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation.

Dat Ngo1, Gi-Dong Lee1, Bongsoon Kang1

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

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a haziness degree evaluator to estimate haze density in images. The model optimizes image saturation, brightness, and sharpness while minimizing the dark channel for accurate haze assessment.

Keywords:
analytical optimizationcorrelation analysishaze densityhaze-relevant featurehaziness degree

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Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Haze, caused by aerosols, degrades image quality by scattering and absorbing light.
  • Reduced visibility from haze impacts critical computer vision tasks like object recognition.
  • Current dehazing methods often lack the ability to adapt to varying haze densities.

Purpose of the Study:

  • To develop a novel model for estimating haze density from single images.
  • To provide a quantitative measure of haziness without requiring reference images or extensive training data.
  • To enable adaptive haze removal and performance assessment.

Main Methods:

  • A haziness degree evaluator model is proposed.
  • The model optimizes an objective function based on image saturation, brightness, and sharpness.
  • Minimization of the dark channel is incorporated into the objective function.

Main Results:

  • The model accurately predicts haze density from single images.
  • The proposed method demonstrates efficacy in hazy/haze-free image classification.
  • The model proves useful for dehazing performance assessment and single image dehazing.

Conclusions:

  • The haziness degree evaluator offers a robust solution for single-image haze density estimation.
  • This approach supports adaptive image processing and evaluation in various computer vision applications.
  • The model's performance is validated through extensive experiments on real and synthetic datasets.