Multiframe Selective Information Fusion from Robust Error Estimation Theory
Document Type
Article
Publication Date
5-2005
Publication Source
IEEE Transactions on Image Processing
Abstract
A dynamic procedure for selective information fusion from multiple image frames is derived from robust error estimation theory. The fusion rate is driven by the anisotropic gain function, defined to be the difference between the Gaussian smoothed-edge maps of a given input frame and of an evolving synthetic output frame. The gain function achieves both selection and rapid fusion of relatively sharper features from each input frame compared to the synthetic frame. Effective applications are demonstrated for image sharpening in imaging through atmospheric turbulence, for multispectral fusion of the RGB spectral components of a scene, for removal of blurred visual obstructions from in front of a distant focused scene, and for high-resolution two-dimensional display of three-dimensional objects in microscopy.
Inclusive pages
577-584
ISBN/ISSN
1057-7149
Copyright
Copyright © 2005, IEEE
Publisher
Institute of Electrical and Electronics Engineers
Volume
14
Issue
5
Peer Reviewed
yes
eCommons Citation
John, Sarah and Vorontsov, Mikhail, "Multiframe Selective Information Fusion from Robust Error Estimation Theory" (2005). Electro-Optics and Photonics Faculty Publications. 107.
https://ecommons.udayton.edu/eop_fac_pub/107
COinS
Comments
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