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Proceedings Paper

Research on compressive fusion by multiwavelet transform
Author(s): Senlin Yang; Guobin Wan; Yuanyuan Li; Xiaoxia Zhao; Xin Chong
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Paper Abstract

A new strategy for images fusion is developed on the basis of block compressed sensing (BCS) and multiwavelet transform (MWT). Since the BCS with structured random matrix requires small memory space and enables fast computation, firstly, the images with large amounts of data can be compressively sampled into block images for fusion. Secondly, taking full advantages of multiwavelet such as symmetry, orthogonality, short support, and a higher number of vanishing moments, the compressive sampling of block images can be better described by MWT transform. Then the compressive measurements are fused with a linear weighting strategy based on MWT decomposition. And finally, the fused compressive samplings are reconstructed by the smoothed projection Landweber algorithm, with consideration of blocking artifacts. Experiment result shows that the validity of proposed method. Simultaneously, field test indicates that the compressive fusion can give similar resolution with traditional MWT fusion.

Paper Details

Date Published: 21 February 2014
PDF: 6 pages
Proc. SPIE 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013, 91420Z (21 February 2014); doi: 10.1117/12.2055321
Show Author Affiliations
Senlin Yang, Xi'an Univ. of Arts and Science (China)
Northwestern Polytechnical Univ. (China)
Guobin Wan, Northwestern Polytechnical Univ. (China)
Yuanyuan Li, Xi'an Univ. of Arts and Science (China)
Xiaoxia Zhao, Xi'an Univ. of Arts and Science (China)
Xin Chong, Emerson Network Power Ltd. (China)


Published in SPIE Proceedings Vol. 9142:
Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
Jorge Ojeda-Castaneda; Shensheng Han; Ping Jia; Jiancheng Fang; Dianyuan Fan; Liejia Qian; Yuqiu Gu; Xueqing Yan, Editor(s)

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