19 September 2016 An adaptive enhancement algorithm for infrared video based on modified k-means clustering
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Abstract
In this paper, we have proposed a video enhancement algorithm to improve the output video of the infrared camera. Sometimes the video obtained by infrared camera is very dark since there is no clear target. In this case, infrared video should be divided into frame images by frame extraction, in order to carry out the image enhancement. For the first frame image, which can be divided into k sub images by using K-means clustering according to the gray interval it occupies before k sub images’ histogram equalization according to the amount of information per sub image, we used a method to solve a problem that final cluster centers close to each other in some cases; and for the other frame images, their initial cluster centers can be determined by the final clustering centers of the previous ones, and the histogram equalization of each sub image will be carried out after image segmentation based on K-means clustering. The histogram equalization can make the gray value of the image to the whole gray level, and the gray level of each sub image is determined by the ratio of pixels to a frame image. Experimental results show that this algorithm can improve the contrast of infrared video where night target is not obvious which lead to a dim scene, and reduce the negative effect given by the overexposed pixels adaptively in a certain range.
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Linze Zhang, Jingqi Wang, Wen Wu, "An adaptive enhancement algorithm for infrared video based on modified k-means clustering", Proc. SPIE 9974, Infrared Sensors, Devices, and Applications VI, 99740Z (19 September 2016); doi: 10.1117/12.2235204; https://doi.org/10.1117/12.2235204
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