IEEE Trans Image Process - Adaptive kernel-based image denoising employing semi-parametric regularization.

Tópicos

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Resumo

The main contribution of this paper is the development of a novel approach, based on the theory of Reproducing Kernel Hilbert Spaces (RKHS), for the problem of noise removal in the spatial domain. The proposed methodology has the advantage that it is able to remove any kind of additive noise (impulse, gaussian, uniform, etc.) from any digital image, in contrast to the most commonly used denoising techniques, which are noise dependent. The problem is cast as an optimization task in a RKHS, by taking advantage of the celebrated Representer Theorem in its semi-parametric formulation. The semi-parametric formulation, although known in theory, has so far found limited, to our knowledge, application. However, in the image denoising problem, its use is dictated by the nature of the problem itself. The need for edge preservation naturally leads to such a modeling. Examples verify that in the presence of gaussian noise the proposed methodology performs well compared to wavelet based technics and outperforms them significantly in the presence of impulse or mixed noise.

Resumo Limpo

main contribut paper develop novel approach base theori reproduc kernel hilbert space rkhs problem nois remov spatial domain propos methodolog advantag abl remov kind addit nois impuls gaussian uniform etc digit imag contrast common use denois techniqu nois depend problem cast optim task rkhs take advantag celebr represent theorem semiparametr formul semiparametr formul although known theori far found limit knowledg applic howev imag denois problem use dictat natur problem need edg preserv natur lead model exampl verifi presenc gaussian nois propos methodolog perform well compar wavelet base technic outperform signific presenc impuls mix nois

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