Research on compressive fusion by multiwavelet transform

Senlin Yang; Guobin Wan; Yuanyuan Li; Xiaoxia Zhao; Xin Chong

doi:10.1117/12.2055321

21 February 2014 Research on compressive fusion by multiwavelet transform

Senlin Yang, Guobin Wan, Yuanyuan Li, Xiaoxia Zhao, Xin Chong

Proceedings Volume 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 (2014) https://doi.org/10.1117/12.2055321
Event: Selected Proceedings of the Photoelectronic Technology Committee Conferences held July-December 2013, 2013, SuZhou, China

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.

Citation Download Citation

Senlin Yang, Guobin Wan, Yuanyuan Li, Xiaoxia Zhao, and Xin Chong "Research on compressive fusion by multiwavelet transform", 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); https://doi.org/10.1117/12.2055321

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KEYWORDS

Image fusion

Image compression

Reconstruction algorithms

Compressed sensing

Digital filtering

Matrices

Image filtering

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