Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces

Details Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces Edge Preserving Image Denoising in Reproducing Kernel Hilbert Spaces

2010-11-27

arxiv.org/abs/1011.5962v1

Proceedings of the 20th International Conference on Pattern Recognition, Istanbul: Turkey, 23-26 August 2010

Computer Science

Computer Vision and Pattern Recognition

This work has been selected for the Best Scientific Paper Award (Track III: Signal, Speech, Image and Video Processing) at the

Scientific paper

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.

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