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14 January 2016 Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis
Zhuozheng Wang, J. R. Deller Jr., Blair D. Fleet
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Journal of Electronic Imaging, Vol. 25, Issue 1, 013007 (January 2016). https://doi.org/10.1117/1.JEI.25.1.013007
Abstract
Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Zhuozheng Wang, J. R. Deller Jr., and Blair D. Fleet "Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis," Journal of Electronic Imaging 25(1), 013007 (14 January 2016). https://doi.org/10.1117/1.JEI.25.1.013007
Published: 14 January 2016
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Cited by 15 scholarly publications.
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KEYWORDS
Image fusion
Principal component analysis
Image processing
Wavelets
Wavelet transforms
Image transmission
Infrared imaging
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