Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1155/ASP/2006/83268 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
irregularly sampled data,local signal,traditional framework,adaptive normalized convolution,image fusion,robust signal certainty,robust fusion,adaptive nc,nc equivalent,robust error norm.,super- resolution,super-resolution image reconstruction,local taylor series expansion,local linear structure,excellent fusion capability,super resolution,image reconstruction,physics,polynomials,approximation theory,taylor series expansion,taylor series,convolution,signal processing,signal to noise ratio | Iterative reconstruction,Image fusion,Computer science,Convolution,Signal-to-noise ratio,Algorithm,Approximation theory,Image processing,Window function,Taylor series | Journal |
Volume | Issue | ISSN |
2006, | 1 | 1687-6180 |
Citations | PageRank | References |
80 | 3.50 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tuan Q. Pham | 1 | 231 | 13.94 |
| Lucas J. van Vliet | 2 | 842 | 113.16 |
| Klamer Schutte | 3 | 173 | 18.26 |