Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Shiqi Huang (), Yucheng Zhang and Ouya Zhang
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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

IJERPH, 2023, vol. 20, issue 4, 1-19

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.

Keywords: remote sensing image; haze removal; gradient feature; guided filtering; dark channel prior method (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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