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Proceedings Paper

An adaptive local contrast enhancement method for low visibility aerial image
Author(s): Yangyang Xu; Xiuhua Zhang; Jian Liu
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Paper Abstract

The contrast and color fidelity of aerial images are usually seriously weakened and many features are covered because of atmospheric scattering and other factors. By using global features, low contrast images can be improved globally and the enhanced images may have little noise and ringing artifacts, but overexposure or underexposure may occur on some parts. By using local features, the details appear better, but it may lead to noise and ringing artifacts when the contrast gain is too large. In the paper, a new contrast enhancement method with adaptive gamma correction based on the weighting of global and local gray-scale mean is proposed. The adaptive gamma parameter which is obtained by incorporating the global and local gray-scale mean into the weighting distribution, is used to correct the gray value of each pixel in the image. Aerial images taken by DJI unmanned aerial vehicle with Inspire 2 at an altitude of 500 meters have been processed in the proposed method. Experimental results indicated that the proposed algorithm performs even better than the current mainstream methods in contrast enhancement for low visibility aerial images.

Paper Details

Date Published: 14 February 2020
PDF: 7 pages
Proc. SPIE 11432, MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 1143209 (14 February 2020); doi: 10.1117/12.2538091
Show Author Affiliations
Yangyang Xu, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and HD Projection (China)
Wuhan Institute of Technology (China)
Xiuhua Zhang, Hubei Key Lab. of Optical Information and Pattern Recognition (China)
Hubei Engineering Research Ctr. of Video Image and HD Projection (China)
Wuhan Institute of Technology (China)
Jian Liu, Wuhan Institute of Technology (China)


Published in SPIE Proceedings Vol. 11432:
MIPPR 2019: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Zhiguo Cao; Jie Ma; Zhong Chen; Yu Shi, Editor(s)

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