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Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Author

Listed:
  • Shiqi Huang

    (School of Information Technology & Engineering, Guangzhou College of Commerce, Guangzhou 511363, China)

  • Yucheng Zhang

    (School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Ouya Zhang

    (School of Information Technology & Engineering, Guangzhou College of Commerce, Guangzhou 511363, China)

Abstract

Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value.

Suggested Citation

  • Shiqi Huang & Yucheng Zhang & Ouya Zhang, 2023. "Image Haze Removal Method Based on Histogram Gradient Feature Guidance," IJERPH, MDPI, vol. 20(4), pages 1-19, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:4:p:3030-:d:1062782
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    Cited by:

    1. Ahmed Alshahir & Khaled Kaaniche & Ghulam Abbas & Paolo Mercorelli & Mohammed Albekairi & Meshari D. Alanazi, 2024. "A Study on the Performance of Adaptive Neural Networks for Haze Reduction with a Focus on Precision," Mathematics, MDPI, vol. 12(16), pages 1-26, August.

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