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Car anti-blooming method based on visible and infrared image fusion

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Category: Information technologies, systems analysis and administration

Last Updated on 06 November 2015

Published on 06 November 2015

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Authors:

Guo Quanmin, Xi’an Technological University, Xi'an 710021, Shannxi, China.

Li Xiaoling, Xi’an Technological University, Xi'an 710021, Shannxi, China.

Abstract:

Purpose. To improve the safety performance at the night driving, this paper researches new car anti-blooming method.

Methodology. According to the characteristics of the color space transformation, the YUV and IHS algorithms were used to disposevisible light image and infrared image, and extract visible light image luminance component and infrared image information by wavelet transform to obtain the new luminance component. Then, the disposed image undergone inverse transformation and fusion disposing to gain new image, and give the detail calculation and disposing function.

Findings. The results ofthe car headlights imagegathering at nightand imageprocessingshow that the visible light and infrared image fusion processing algorithms can eliminate anti-blooming phenomenon and retain the image details, which can effectively weaken the anti-bloomingeffect.

Originality. The color space transform and wavelet fusion algorithm, IHS and wavelet transform fusion algorithm can better improve the image spectral distortion of car headlights at night, at the same time, YUV transform and wavelet transform fu-sion algorithm can obtain better edge information. This method is a new technology in the car research field.

Practical value. The proposed algorithm can be applied to automobile safe driving, and has a high application value.

References:

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2. Zhang, Y. and Hong, G.(2005),“An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural color IKONOS and Quickbirdimages ” ,Information Fusion, vol. 6, no. 3, pp. 225–234.

3. Choi, M.(2001),“A New Look at IHS-like Image Fusion Methods” ,Information Fusion, vol.8, no.2, pp. 177–186.

4. Hong, G., Zhang, Y. and Mercer, B.(2009),“A Wavelet and IHS Integration Method to Fuse High Resolution SAR with Moderate Resolution Multispectral Images”, Photogrammetric Engineering & Remote Sensing, Vol.75, no.10, pp.1213–1223.

5. Deng, L. and Chen, Y.H.(2005), “Controllable remote sensing image fusion method based on wavelet transform”, Journal of Infrared and Millimeter Waves, vol. 24, no.1, pp. 34–38.

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7. Xing, Y.Q., Wang, X.D. and Bi, K.(2014),“Fusion technique for grey-scale visible light and infrared images based on independent component analysis and intensity-hue-saturation transform”, Control and Decision, vol. 29, no. 3, pp. 411–417.

8. Zhang, J. and Zhang, J.P.(2008),“Remote sensing image fusion method based on IHS transform”, Journal of Liaoning Technical University,vol.27, no. 3, pp. 350–352.

9. Qian, X., Han, L. and Wang, B.(2011),“A Fast Fusion Algorithm of Visible and Infrared Images”, Journal of Computer-Aided Design & Computer Graphics, vol. 23, no. 7, pp. 1211–1216.

10. Ma, S., Fang, J. and Sun, S.(2009),“Colorizing Algorithm of Night-Vision Image Based on Clustering of False Color Fused Image”,ActaOpticaSinica, vol. 29, no. 6, pp. 1502–1507.

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