An Improved Local Equilibrium Contrast Enhancement Algorithm for Infrared Laser Images

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Yuhong Li 1,* Jianzhong Zhou 1 Wei Ding 1 Shan Ding 1

1. Digital Engineering and Simulation Centre Huazhong University of Science and Technology Wuhan 430074, China

* Corresponding author.


Received: 20 Aug. 2010 / Revised: 5 Oct. 2010 / Accepted: 18 Nov. 2010 / Published: 8 Dec. 2010

Index Terms

Infrared Laser Images, Grey Transformation, Evaluation of Image Quality, Self-adaptive Threshold


An improved local equilibrium contrast enhancement algorithm based self-adaptive contrast enhancement algorithm is proposed for infrared laser images, in which the image pixel value histogram is divided into three parts: background and noise area, targets area, and uninterested area. The targets parts are highlighted, while the background and noise parts and the uninterested parts are restrained. A comprehensive qualitative and quantitative image enhancement performance evaluation is presented to verify the proposed theory and algorithm validity, efficiency and reasonability. The experimental results indicate that the proposed algorithm can greatly improve the global and local contrast for both near infrared images and far infrared laser images while efficiently reducing noise in the infrared laser images´╝îand the visual quality of enhanced image is obviously better than the enhancement of the traditional histogram equalization, double plateaus histogram equalization algorithm, etc.

Cite This Paper

Yuhong Li,Jianzhong Zhou,Wei Ding,Shan Ding, "An Improved Local Equilibrium Contrast Enhancement Algorithm for Infrared Laser Images", IJIGSP, vol.2, no.2, pp.32-38, 2010. DOI: 10.5815/ijigsp.2010.02.05


[1]Zhang CJ, Wang XD, Zhang HR, et al.: An anti-noise algorithm for enhancing global and local contrast for infrared image, International Journal of Wavelets Multiresulution and Information Processing, Vol.5(1), 101-112 (2007)

[2]Song, Y.F.; Shao, X.P.; Xu, J.; New enhancement algorithm for infrared image based on double plateaus histogram, Infrared and Laser Engineering (in Chinese), Vol.37(2), 2008:308-311

[3]Wallis, R.; An approach to the Space Variant Restoration and enhancement of images, Proc Symp on Current Mathematical Problems in Image Science, Navel Postgraduate School, Monterey, CA, 235-241(1976)

[4]Atutaleb, S. A.; Automatic thresholding of gray-level pictures using two-dimension entropy, Computer Vision, Graphics and Image Processing, 47(1): 22-32(1989)

[5]Highnam, R.; Brady, M.; Model-based image enhancement of far infrared images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.19(4), 410 – 415 (1997)

[6]Tang, M.; Ma, S.D.; and Xiao, J.; Model-based adaptive enhancement of far infrared image sequences, Pattern Recognition Letters, Vol.21(9), 827-835(2000)

[7]Gilboa, G.; Sochen, N.; Zeevi, Y. Y.; Forward-and-backward diffusion processes for adaptive image enhancement and denoising, IEEE Trans. Image Process., vol. 11, no. 7, 689–703(2002)

[8]Li, Y.H.; Wang, C.; Zhou, J.Z.; Liu, Q.M.; Real-time Infrared Laser Measurement System Design and Development, Proceedings of 2009 WRI World Congress on Software Engineering, Vol. 1, 435-439(2009)

[9]Rosenfield, A.; Kak, A.C.; Digital Picture Processing, New York, Academic Press, 1982.

[10]Zhang, C.J.; Yang, F.; Wang, X.D.; Zhang, H.R.;An Efficient Nonlinear Algorithm for Contrast Enhancement of Infrared Image, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 18-21 August 2005.

[11]Yeong-Taeg Kim, IEEE Transactions on Consumer Electronics, Vol. 43, No. 1, FEBRUARY 1997