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Classification-based image-fusion framework for compressive imaging

[+] Author Affiliations
Xiaoyan Luo

Beihang University, School of Electronics and Information Engineering, Beijing 100191, China

Jun Zhang

Beihang University, School of Electronics and Information Engineering, Beijing 100191, China

Jingyu Yang

Tianjin University, School of Electronics and Information Engineering, Tianjin 300072, China

Qionghai Dai

Tsinghua University, Department of Automation, Tsinghua National Laboratory for Information Science and Technology (TNList), Beijing 100084, China

J. Electron. Imaging. 19(3), 033009 (August 17, 2010). doi:10.1117/1.3478879
History: Received November 05, 2009; Revised June 23, 2010; Accepted June 28, 2010; Published August 17, 2010; Online August 17, 2010
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We propose a novel image-fusion framework for compressive imaging (CI), which is a new technology for simultaneous sampling and compressing of images based on the principle of compressive sensing (CS). Unlike previous fusion work operated on conventional images, we directly perform fusion on the measurement vectors from multiple CI sensors according to the similarity classification. First, we define a metric to evaluate the data similarity of two given CI measurement vectors and present its potential advantage for classification. Second, the fusion rules for CI measurement vectors in different similarity types are investigated to generate a comprehensive measurement vector. Finally, the fused image is reconstructed from the combined measurements via an optimization algorithm. Simulation results demonstrate that the reconstructed images in our fusion framework are visually more appealing than the fused images using other fusion rules, and our fusion method for CI significantly saves computational complexity against the fusion-after-reconstruction scheme.

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© 2010 SPIE and IS&T

Citation

Xiaoyan Luo ; Jun Zhang ; Jingyu Yang and Qionghai Dai
"Classification-based image-fusion framework for compressive imaging", J. Electron. Imaging. 19(3), 033009 (August 17, 2010). ; http://dx.doi.org/10.1117/1.3478879


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