Infrared Image and Visible Image Fusion Based on Wavelet Transform

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Abstract:

The same scene, the infrared image and visible image fusion can concurrently take advantage of the original image information can overcome the limitations and differences of a single sensor image in terms of geometric, spectral and spatial resolution, to improve the quality of the image , which help to locate, identify and explain the physical phenomena and events. Put forward a kind of image fusion method based on wavelet transform. And for the wavelet decomposition of the frequency domain, respectively, discussed the principles of select high-frequency coefficients and low frequency coefficients, highlight the contours of parts and the weakening of the details section, fusion, image fusion has the characteristics of two or multiple images, more people or the visual characteristics of the machine, the image for further analysis and understanding, detection and identification or tracking of the target image.

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Periodical:

Advanced Materials Research (Volumes 756-759)

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2850-2856

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September 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] GEMMA P, A general framework for multiresolution image fusion: from pixels to regions , Information Fusion. America, vol. 4, pp.295-280, april (2003).

DOI: 10.1016/s1566-2535(03)00046-0

Google Scholar

[2] BURT P T,and ADELSON E H, The Laplacian pyramid as a compact image code, IEEE Trans on Communications. America, vol. 31, pp.532-540, april (1983).

DOI: 10.1109/tcom.1983.1095851

Google Scholar

[3] CHEN Hao, and WANG Yan-jie, Study for Image Fusion Based on Wavelet Transform, MICROELECTRONICS & COMPUTER. China, vol. 27, pp.39-41, may (2010).

Google Scholar

[4] Raghavendra R, Dorizzi B, and Rao A, Particle Swarm Optimization based Fusion of Near Infrared and Visible Images for Improved Face Verfication, Pattern Recognition. America, vol. 44, pp.401-411, february (2011).

DOI: 10.1016/j.patcog.2010.08.006

Google Scholar

[5] ZHAO Ying-nan, WEN Xue-zhi, and Cheng Ya-ping, Fase Face Recognition of Sparse Representation Based Fusion of Visible and Near Infrared Images, Computer Science. China, vol. 39, pp.270-273, june (2012).

Google Scholar

[6] CAO Yi-qin, LEI Zhang-ming, and HUANG Xiao-sheng, Region-based algorithm for non-sampling morphologicalwavelet medical image fusion, Application Research of Computers. China, vol. 29, pp.2379-2381, june (2012).

Google Scholar

[7] ZHU Ya-hui, and PENG Guo-hua, Evaluation method of image fusion based on multi-scale PSNR, Application Research of Computers. China, vol. 29, pp.2784-2786, july (2012).

Google Scholar