Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

Title

Robust Fusion of Irregularly Sampled Data Using Adaptive Normalized Convolution

 Author

Pham, T.Q.
Van Vliet, L.J.
Schutte, K.

Faculty

Applied Sciences

 Date

2006

 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.

 To reference this document use:

http://resolver.tudelft.nl/uuid:80b5f75f-5bbe-4f77-88e5-8f7acc7e8351

 Publisher

Hindawi Publishing Corporation

 ISSN

1687-6180

 Source

EURASIP Journal on Applied Signal Processing, 2006

 Part of collection

Institutional Repository

 Document type

journal article

 Rights

(c) 2006 Pham, T.Q.; Van Vliet, L.J.; Schutte, K.
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