Robust fusion of irregularly sampled data using adaptive normalized convolution

Authors:
Tuan Q. Pham;Lucas J. van Vliet;Klamer Schutte
Affiliations:
Quantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg, Delft, the Netherlands;Quantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, Lorentzweg, Delft, the Netherlands;Electro Optics Group, TNO Defence, Security, and Safety, the Hague, the Netherlands
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

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Abstract

We present a novel algorithm for image fusion from irregularly sampled data. The method is based on the framework of normalized convolution (NC), in which the local signal is approximated through a projection onto a subspace. The use of polynomial basis functions in this paper makes NC equivalent to a local Taylor series expansion. Unlike the traditional framework, however, the window function of adaptive NC is adapted to local linear structures. This leads to more samples of the same modality being gathered for the analysis, which in turn improves signal-to-noise ratio and reduces diffusion across discontinuities. A robust signal certainty is also adapted to the sample intensities to minimize the influence of outliers. Excellent fusion capability of adaptive NC is demonstrated through an application of super-resolution image reconstruction.