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Image Dehazing Using LiDAR Generated Grayscale Depth Prior.

Won Young Chung1, Sun Young Kim2, Chang Ho Kang3

  • 1Department of Aerospace Engineering, Automation and System Research Institute, Seoul National University, Seoul 08826, Korea.

Sensors (Basel, Switzerland)
|February 15, 2022
PubMed
Summary

This study introduces a novel dehazing algorithm using LiDAR depth images. It accurately estimates scattering coefficients for improved image clarity, achieving a 24% average performance increase in SSIM.

Keywords:
LiDARdehazingdepthscattering coefficient

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Area of Science:

  • Computer Vision
  • Image Processing
  • Remote Sensing

Background:

  • Haze significantly degrades image quality, impacting visibility and analysis.
  • Existing dehazing methods often struggle with accurate atmospheric parameter estimation.
  • Depth information from LiDAR offers a promising avenue for robust dehazing.

Purpose of the Study:

  • To develop an effective image dehazing algorithm utilizing LiDAR-derived depth information.
  • To accurately estimate the scattering coefficient crucial for depth image-based dehazing.
  • To improve quantitative and qualitative image quality metrics post-dehazing.

Main Methods:

  • Generated a one-channel grayscale depth image from LiDAR point cloud 2D projection.
  • Estimated optimal scattering coefficients by analyzing distributions with dark channels in synthetic haze images.
  • Developed a linear regression model to establish a relationship between scattering coefficients and dark channels.
  • Applied the atmospheric scattering model using estimated coefficients and depth images for dehazing.

Main Results:

  • Successfully estimated optimal scattering coefficients based on haze levels.
  • Established a predictive equation for scattering coefficients using dark channel features.
  • Demonstrated significant performance improvements in quantitative metrics, with an average SSIM increase of 24%.
  • Qualitative analysis using YOLO v3 confirmed enhanced object detection capabilities.

Conclusions:

  • The proposed LiDAR depth-based dehazing method effectively enhances image quality.
  • Accurate scattering coefficient estimation is vital for successful depth image dehazing.
  • The method shows strong potential for applications requiring clear imagery in adverse weather conditions.