Application of improved wavelet transform algorithm in image fusion

GAO Ying; WANG A-min; WANG Feng-hua; GUO Shu-xia

doi:10.7510/jgjs.issn.1001-3806.2013.05.028

Volume 37 Issue 5

Jul. 2013

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Application of improved wavelet transform algorithm in image fusion

1.
School of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China;
2.
Science and Technology on UAV Laboratory, Northwestern Polytechnical University, Xi'an 710065, China

Received Date: 2012-12-20
Accepted Date: 2013-03-07

Abstract

In order to overcome the defects of fuzzy detection, low recognition rate and poor real-time of traditional fusion methods used in precision-guided weapons systems, a new image fusion algorithm was proposed combining wavelet transform with Canny operator. Firstly, the source image was decomposed into 3 layers in vertical and horizontal directions, which are suitable for image reconstruction; then due to its own characteristics of the different frequency components, an unique fusion rule was used to change wavelet coefficients of images, that is, for the low frequency components, the weighted average fusion algorithm was adopted, and for the high-frequency components, wavelet coefficients were changed using Canny operator and the local area variance criteria method. Finally, images were reconstructed using the inverse wavelet transform for different components. Results show the improved method not only reduces the fuzziness of edge, highlights target color, gets better visual effects, but also makes computational efficiency high, real-time good, particularly can detect and recognize pretend targets. It has better theoretical research and application value.
- image processing,
- image fusion,
- edge detection,
- wavelet transform,
- image evaluation

References

[1]
[2]	LIU S T, ZHOU X D. Recent development of image fusion techniques
[J]. Laser Infrared,2006,36(8):627-631(in Chinese).
[4]
[5]	ZHUANG Y F. Research on image edge detection based on wavelet transform and its applications
[D]. Harbin: Harbin Institute of Technology,2007:18-26(in Chinese).
[7]
[8]	WANG W L. On infrared image processing based on wavelet transform
[D]. Xi'an: Xi'an University of Electronic Science and Technology,2008:3-9(in Chinese).
[10]
[11]	WANG X S, SONG K. The algorithm of edge detection based on multiscale wavelet transform
[J]. Transactions of Shenyang Ligong University,2008,27(4):16-19(in Chinese).
[13]
[14]	ZHANG Y, HONG G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and quick bird images
[J]. Information Fusion,2005,6(3):225-234.
[16]
[17]	XIAO H. Research of infrared and visible image fusion algorithm based on wavelet transform
[D]. Changchun: Changchun University of Science and Technology,2009:23-31(in Chinese).
[19]
[20]	DI H W, ZHANG W I. Application of wavelet edge detection based on Canny criteria in image fusion
[J]. Opto-Electronic Engineering,2005,32(6):79-82(in Chinese).
[22]
[23]	LI H H, GUO L, LIU H. Current research on wavelet-based image fusion algorithms
[J]. Proceedings of SPIE,2005,5813:360-367.
[25]
[26]	CHANG H W, LAN Sh D. Image fusion based on addition of wavelet coefficients
[C]//International Conference on Wavelet Analysis and Pattern Recognition. Beijing, China: IEEE,2007:1585-1588.
[28]
[29]	LIAN J, WANG K, LI G X. Edge-based image fusion algorithm with wavelet transform
[J]. Journal on Communications,2007,28(4):18-23(in Chinese).
[31]
[32]	ZENG H, LI Y X, WANG Q. Some applications in image procession with wavelets
[C]//Proceedings of 2010 Asia-Pacific Youth Conference on Communication. Kunming, China:IEEE,2010:356-359.
[34]
[35]	NIU Y F, XU Sh T, HU W D. Fusion of infrared and visible image based on target regions for environment perception
[J]. Applied Mechanics and Materials,2012,128/129:589-593.
[37]
[38]	DENG A, WU JI Y Sh. An image fusion algorithm based on discrete wavelet transform and canny operator
[C]//Communications in Computer and Information Science 2011. Wuhan, China: Springer,2011,175: 32-38.
[40]
[41]	HU L M, GAO J, HE K F. Research on quality measures for image fusion
[J]. Electronics,2004,32(s1):218-221(in Chinese)
[43]
[44]	WEN C Y, CHEN J K. Multi-resolution image fusion technique and its application to forensic science
[J]. Forensic Science International,2004,140(2/3):217-232.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Application of improved wavelet transform algorithm in image fusion

1. School of Marine Engineering, Northwestern Polytechnical University, Xi'an 710072, China;

2. Science and Technology on UAV Laboratory, Northwestern Polytechnical University, Xi'an 710065, China

Received Date: 2012-12-20

Accepted Date: 2013-03-07

Available Online: 2013-09-25

Abstract: In order to overcome the defects of fuzzy detection, low recognition rate and poor real-time of traditional fusion methods used in precision-guided weapons systems, a new image fusion algorithm was proposed combining wavelet transform with Canny operator. Firstly, the source image was decomposed into 3 layers in vertical and horizontal directions, which are suitable for image reconstruction; then due to its own characteristics of the different frequency components, an unique fusion rule was used to change wavelet coefficients of images, that is, for the low frequency components, the weighted average fusion algorithm was adopted, and for the high-frequency components, wavelet coefficients were changed using Canny operator and the local area variance criteria method. Finally, images were reconstructed using the inverse wavelet transform for different components. Results show the improved method not only reduces the fuzziness of edge, highlights target color, gets better visual effects, but also makes computational efficiency high, real-time good, particularly can detect and recognize pretend targets. It has better theoretical research and application value.

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Reference (45)

[1]
[2]	LIU S T, ZHOU X D. Recent development of image fusion techniques
[3]	[J]. Laser Infrared,2006,36(8):627-631(in Chinese).
[4]
[5]	ZHUANG Y F. Research on image edge detection based on wavelet transform and its applications
[6]	[D]. Harbin: Harbin Institute of Technology,2007:18-26(in Chinese).
[7]
[8]	WANG W L. On infrared image processing based on wavelet transform
[9]	[D]. Xi'an: Xi'an University of Electronic Science and Technology,2008:3-9(in Chinese).
[10]
[11]	WANG X S, SONG K. The algorithm of edge detection based on multiscale wavelet transform
[12]	[J]. Transactions of Shenyang Ligong University,2008,27(4):16-19(in Chinese).
[13]
[14]	ZHANG Y, HONG G. An IHS and wavelet integrated approach to improve pan-sharpening visual quality of natural colour IKONOS and quick bird images
[15]	[J]. Information Fusion,2005,6(3):225-234.
[16]
[17]	XIAO H. Research of infrared and visible image fusion algorithm based on wavelet transform
[18]	[D]. Changchun: Changchun University of Science and Technology,2009:23-31(in Chinese).
[19]
[20]	DI H W, ZHANG W I. Application of wavelet edge detection based on Canny criteria in image fusion
[21]	[J]. Opto-Electronic Engineering,2005,32(6):79-82(in Chinese).
[22]
[23]	LI H H, GUO L, LIU H. Current research on wavelet-based image fusion algorithms
[24]	[J]. Proceedings of SPIE,2005,5813:360-367.
[25]
[26]	CHANG H W, LAN Sh D. Image fusion based on addition of wavelet coefficients
[27]	[C]//International Conference on Wavelet Analysis and Pattern Recognition. Beijing, China: IEEE,2007:1585-1588.
[28]
[29]	LIAN J, WANG K, LI G X. Edge-based image fusion algorithm with wavelet transform
[30]	[J]. Journal on Communications,2007,28(4):18-23(in Chinese).
[31]
[32]	ZENG H, LI Y X, WANG Q. Some applications in image procession with wavelets
[33]	[C]//Proceedings of 2010 Asia-Pacific Youth Conference on Communication. Kunming, China:IEEE,2010:356-359.
[34]
[35]	NIU Y F, XU Sh T, HU W D. Fusion of infrared and visible image based on target regions for environment perception
[36]	[J]. Applied Mechanics and Materials,2012,128/129:589-593.
[37]
[38]	DENG A, WU JI Y Sh. An image fusion algorithm based on discrete wavelet transform and canny operator
[39]	[C]//Communications in Computer and Information Science 2011. Wuhan, China: Springer,2011,175: 32-38.
[40]
[41]	HU L M, GAO J, HE K F. Research on quality measures for image fusion
[42]	[J]. Electronics,2004,32(s1):218-221(in Chinese)
[43]
[44]	WEN C Y, CHEN J K. Multi-resolution image fusion technique and its application to forensic science
[45]	[J]. Forensic Science International,2004,140(2/3):217-232.

Application of improved wavelet transform algorithm in image fusion

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通讯作者: 陈斌, bchen63@163.com

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Application of improved wavelet transform algorithm in image fusion

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