Brightness and Color Transfer for Infrared Images in Vehicle Night Vision System

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The automotive night vision system can significantly improve the safety of driving at night by fusion of visible and infrared images. Due to the aim at helping driving at night, it’s important to develop the visibility and the consistency of the image and the real condition on the road. In this paper, visible light image is enhanced its light and shadow information based on nonlinear exponential function and the infrared image is increased its contrast by S-Retriex method. Then the enhancement images are fused and the color space of fusion image is adjusted by the reference statistical characteristics. Simulations have shown that the result image has good visual scene which retains the details of the infrared image and has original light, shadow and color information.

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2477-2482

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December 2012

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] X. B. Jin and Q. L. Zhang, EM image fusion algorithm based on statistical signal processing, Proceedings of the 2nd International Congress on Image and Signal Processing (2009), pp.3548-3551

DOI: 10.1109/cisp.2009.5302754

Google Scholar

[2] A. Toet, M. A. Hogervorst, S. G. Nikolov, J. J. Lewis, T. D. Dixon, D. R. Bull, and C. N. Canagarajah, Towards cognitive image fusion, Information Fusion, vol. 11(2010), pp.95-113

DOI: 10.1016/j.inffus.2009.06.008

Google Scholar

[3] X. Li and S. Qin, Efficient fusion for infrared and visible images based on compressive sensing principle, IET Image Processing, vol. 5(2011), pp.141-147

DOI: 10.1049/iet-ipr.2010.0084

Google Scholar

[4] X. Ding, G. Xu, Z. Wang, J. Wu, A. Yang, and Z. Xue, Application of image fusion in intelligent transport system, International Journal of Digital Content Technology and its Applications, vol. 6(2012), pp.413-420

Google Scholar

[5] H. Zhou, An stationary wavelet transform and curvelet transform based infrared and visible images fusion algorithm, International Journal of Digital Content Technology and its Applications, vol. 6(2012), pp.144-151

DOI: 10.4156/jdcta.vol6.issue1.18

Google Scholar

[6] X. J. Lu, M. X. Ding, and Y.-K. Wang, New pseudo-color transform for fibre masses inspection of industrial images, Zidonghua Xuebao/ Acta Automatica Sinica, vol. 35(2009), pp.233-238

DOI: 10.3724/sp.j.1004.2009.00233

Google Scholar

[7] T. Pouli and E. Reinhard, Progressive color transfer for images of arbitrary dynamic range, Computers and Graphics (Pergamon), vol. 35(2011), pp.67-80

DOI: 10.1016/j.cag.2010.11.003

Google Scholar

[8] X. Jin, J. Bao, and J. Du, Image enhancement based on selective - Retinex Fusion algorithm, Journal of Software, vol. 7(2012), pp.1187-1194

DOI: 10.4304/jsw.7.6.1187-1194

Google Scholar

[9] X.-B. Jin, H.-J. Zheng, J.-J. Du, and Y.-M. Wang, S-Retinex brightness adjustment for night-vision image, Procedia Engineering, vol. 15(2011), pp.2571-2576

DOI: 10.1016/j.proeng.2011.08.483

Google Scholar

[10] S. Yin, L. Cao, Y. Ling, and G. Jin, One color contrast enhanced infrared and visible image fusion method, Infrared Physics and Technology, vol. 53(2010), pp.146-150

DOI: 10.1016/j.infrared.2009.10.007

Google Scholar

[11] A. Toet and M. A. Hogervorst, Progress in color night vision, Optical Engineering, vol. 51(2012), p.19

Google Scholar

[12] Toet. Natural colour mapping for multiband nightvision imagery, Information Fusion, vol. 4(2003), pp.155-166

DOI: 10.1016/s1566-2535(03)00038-1

Google Scholar