Metadata-assisted nonuniform atmospheric scattering model of image haze removal for medium-altitude unmanned aerial vehicle

Chunlei Liu; Wenrui Ding; Hongguang Li; Jiankun Li

doi:10.1117/1.JEI.26.5.053009

12 September 2017 Metadata-assisted nonuniform atmospheric scattering model of image haze removal for medium-altitude unmanned aerial vehicle

Chunlei Liu, Wenrui Ding, Hongguang Li, Jiankun Li

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 5, 053009 (September 2017). https://doi.org/10.1117/1.JEI.26.5.053009

Abstract

Haze removal is a nontrivial work for medium-altitude unmanned aerial vehicle (UAV) image processing because of the effects of light absorption and scattering. The challenges are attributed mainly to image distortion and detail blur during the long-distance and large-scale imaging process. In our work, a metadata-assisted nonuniform atmospheric scattering model is proposed to deal with the aforementioned problems of medium-altitude UAV. First, to better describe the real atmosphere, we propose a nonuniform atmospheric scattering model according to the aerosol distribution, which directly benefits the image distortion correction. Second, considering the characteristics of long-distance imaging, we calculate the depth map, which is an essential clue to modeling, on the basis of UAV metadata information. An accurate depth map reduces the color distortion compared with the depth of field obtained by other existing methods based on priors or assumptions. Furthermore, we use an adaptive median filter to address the problem of fuzzy details caused by the global airlight value. Experimental results on both real flight and synthetic images demonstrate that our proposed method outperforms four other existing haze removal methods.

Citation Download Citation

Chunlei Liu, Wenrui Ding, Hongguang Li, and Jiankun Li "Metadata-assisted nonuniform atmospheric scattering model of image haze removal for medium-altitude unmanned aerial vehicle," Journal of Electronic Imaging 26(5), 053009 (12 September 2017). https://doi.org/10.1117/1.JEI.26.5.053009

Received: 11 May 2017; Accepted: 17 August 2017; Published: 12 September 2017

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