Title

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

Comments

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Permission documentation on file.

Publisher

Institute of Electrical and Electronics Engineers

Volume

14

Issue

5

Peer Reviewed

yes

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