  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 semiparametric Representer Theorem. Examples verify
that in the presence of gaussian noise the proposed method performs relatively
well compared to wavelet based technics and outperforms them significantly in
the presence of impulse or mixed noise.
  A more detailed version of this work has been published in the IEEE Trans.
Im. Proc. : P. Bouboulis, K. Slavakis and S. Theodoridis, Adaptive Kernel-based
Image Denoising employing Semi-Parametric Regularization, IEEE Transactions on
Image Processing, vol 19(6), 2010, 1465 - 1479.
