Abstract
The images captured in sand-dust weather have the characteristics of color deviation and low visibility, which seriously affect computer vision systems. To solve the above problems, we propose a fast and effective algorithm to enhance the images captured in sand-dust weather conditions. First, we compensate for the loss value in the blue channel. Then, white balancing technology is used to correct the color of the sand-dust-degraded image. Finally, guided image filtering is used to enhance the image contrast and edge accuracy, and an adaptive method is used to calculate the magnification factor of the detail layer to enhance the image detail information. The experimental results on a large number of sand-dust-degraded images show that the method can effectively recover the fading characteristics of sand-dust-degraded images in a short time and improve the clarity of the images. Experimental results via qualitative and quantitative evaluations demonstrate that the proposed method can significantly improve the images captured during sand-dust weather conditions, and the results are better than those of other methods.
Highlights
The images captured in sand-dust weather have the characteristics of low contrast, color deviation and blur, which seriously affect the clarity of the image
PROPOSED METHOD In this study, we propose a visibility restoration algorithm based on blue channel compensation and guided image filtering that is used to enhance the degraded images captured under sand-dust weather conditions
The proposed method consists of two parts: white balancing based on blue channel compensation and guided image filtering
Summary
The images captured in sand-dust weather have the characteristics of low contrast, color deviation and blur, which seriously affect the clarity of the image. This prior work to improve the clarity of sanddust images can be divided into three classes. Current sand-dust-degraded image enhancement algorithms can resolve color deviations, enhance image contrast and improve image clarity, but some problems remain. When using the current color correction technology to process the sand-dust-degraded images, blue artifacts appear in the images and reduce the image quality. The current sand-dust-degraded image enhancement algorithms have a high time complexity
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