IEEE Trans Image Process - Image noise reduction via geometric multiscale ridgelet support vector transform and dictionary learning.

Tópicos

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Resumo

Advances in machine learning technology have made efficient image denoising possible. In this paper, we propose a new ridgelet support vector machine (RSVM) for image noise reduction. Multiscale ridgelet support vector filter (MRSVF) is first deduced from RSVM, to produce a multiscale, multidirection, undecimated, dyadic, aliasing, and shift-invariant geometric multiscale ridgelet support vector transform (GMRSVT). Then, multiscale dictionaries are learned from examples to reduce noises existed in GMRSVT coefficients. Compared with the available approaches, the proposed method has the following characteristics. The proposed MRSVF can extract the salient features associated with the linear singularities of images. Consequently, GMRSVT can well approximate edges, contours and textures in images, and avoid ringing effects suffered from sampling in the multiscale decomposition of images. Sparse coding is explored for noise reduction via the learned multiscale and overcomplete dictionaries. Some experiments are taken on natural images, and the results show the efficiency of the proposed method.

Resumo Limpo

advanc machin learn technolog made effici imag denois possibl paper propos new ridgelet support vector machin rsvm imag nois reduct multiscal ridgelet support vector filter mrsvf first deduc rsvm produc multiscal multidirect undecim dyadic alias shiftinvari geometr multiscal ridgelet support vector transform gmrsvt multiscal dictionari learn exampl reduc nois exist gmrsvt coeffici compar avail approach propos method follow characterist propos mrsvf can extract salient featur associ linear singular imag consequ gmrsvt can well approxim edg contour textur imag avoid ring effect suffer sampl multiscal decomposit imag spars code explor nois reduct via learn multiscal overcomplet dictionari experi taken natur imag result show effici propos method

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