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Journal of Electronic Imaging • Open Access

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis
Author(s): Zhuozheng Wang; J. R. Deller; Blair D. Fleet

Paper 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.

Paper Details

Date Published: 14 January 2016
PDF: 9 pages
J. Electron. Imaging. 25(1) 013007 doi: 10.1117/1.JEI.25.1.013007
Published in: Journal of Electronic Imaging Volume 25, Issue 1
Show Author Affiliations
Zhuozheng Wang, Beijing Univ. of Technology (China)
J. R. Deller, Michigan State Univ. (United States)
Blair D. Fleet, Michigan State Univ. (United States)


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