Name
Abstract | ||
|---|---|---|
The goal of this paper is the development of a novel approach for the problem of Noise Removal, based on the theory of Reproducing Kernels Hilbert Spaces (RKHS). The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated semi parametric Representer Theorem. Examples verify that in the presence of gaussian noise the proposed method performs relatively well compared to wavelet based techniques and outperforms them significantly in the presence of impulse or mixed noise. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICPR.2010.652 | international conference on pattern recognition |
Keywords | DocType | Volume |
novel approach,noise removal,reproducing kernels hilbert spaces,gaussian noise,celebrated semi parametric,mixed noise,optimization task,representer theorem,reproducing kernel hilbert spaces,edge preserving image,hilbert space,image processing,reproducing kernel hilbert space,pattern recognition,noise reduction,noise,kernel,wavelet transforms,hilbert spaces,pixel | Conference | abs/1011.5962 |
ISSN | ISBN | Citations |
1051-4651 | 978-1-4244-7542-1 | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pantelis Bouboulis | 1 | 171 | 11.05 |
| Sergios Theodoridis | 2 | 1353 | 106.97 |
| Konstantinos Slavakis | 3 | 583 | 40.76 |