Print Email Facebook Twitter 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. TNO Defensie en Veiligheid Publication year 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. Subject PhysicsApproximation theoryImage reconstructionOptical resolving powerPolynomialsSignal processingSignal to noise ratioData samplingNormalized convolution (NC)Robust signal certaintyTaylor seriesConvolution To reference this document use: http://resolver.tudelft.nl/uuid:b83c4d66-3595-4aec-b1a4-a9d5b3820b91 DOI https://doi.org/10.1155/asp/2006/83268 TNO identifier 219127 Source EURASIP Journal on Applied Signal Processing, 1-12 Article number 83268 Document type article Files To receive the publication files, please send an e-mail request to TNO Library.