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Proceedings Paper

An adaptive algorithm for low contrast infrared image enhancement
Author(s): Sheng-dong Liu; Cheng-yuan Peng; Ming-jia Wang; Zhi-guo Wu; Jia-qi Liu
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Paper Abstract

An adaptive infrared image enhancement algorithm for low contrast is proposed in this paper, to deal with the problem that conventional image enhancement algorithm is not able to effective identify the interesting region when dynamic range is large in image. This algorithm begin with the human visual perception characteristics, take account of the global adaptive image enhancement and local feature boost, not only the contrast of image is raised, but also the texture of picture is more distinct. Firstly, the global image dynamic range is adjusted from the overall, the dynamic range of original image and display grayscale form corresponding relationship, the gray scale of bright object is raised and the the gray scale of dark target is reduced at the same time, to improve the overall image contrast. Secondly, the corresponding filtering algorithm is used on the current point and its neighborhood pixels to extract image texture information, to adjust the brightness of the current point in order to enhance the local contrast of the image. The algorithm overcomes the default that the outline is easy to vague in traditional edge detection algorithm, and ensure the distinctness of texture detail in image enhancement. Lastly, we normalize the global luminance adjustment image and the local brightness adjustment image, to ensure a smooth transition of image details. A lot of experiments is made to compare the algorithm proposed in this paper with other convention image enhancement algorithm, and two groups of vague IR image are taken in experiment. Experiments show that: the contrast ratio of the picture is boosted after handled by histogram equalization algorithm, but the detail of the picture is not clear, the detail of the picture can be distinguished after handled by the Retinex algorithm. The image after deal with by self-adaptive enhancement algorithm proposed in this paper becomes clear in details, and the image contrast is markedly improved in compared with Retinex algorithm.

Paper Details

Date Published: 21 August 2013
PDF: 7 pages
Proc. SPIE 8908, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications, 890810 (21 August 2013); doi: 10.1117/12.2033010
Show Author Affiliations
Sheng-dong Liu, The National Key Lab. of Computational Mathematics and Experimental Physics (China)
Beijing Institute of Space Long March Vehicle (China)
Cheng-yuan Peng, The National Key Lab. of Computational Mathematics and Experimental Physics (China)
Beijing Institute of Space Long March Vehicle (China)
Ming-jia Wang, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Zhi-guo Wu, Changchun Institute of Optics, Fine Mechanics and Physics (China)
Jia-qi Liu, The National Key Lab. of Computational Mathematics and Experimental Physics (China)
Beijing Institute of Space Long March Vehicle (China)


Published in SPIE Proceedings Vol. 8908:
International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Sensors and Applications
Jun Ohta; Nanjian Wu; Binqiao Li, Editor(s)

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