Optimal spatial adaptation for patch-based image denoising.

Charles Kervrann; Jérôme Boulanger

doi:10.1109/tip.2006.877529

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Optimal spatial adaptation for patch-based image denoising.

Charles Kervrann·Jérôme Boulanger

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Abstract

A novel adaptive and patch-based approach is proposed for image denoising and representation. The method is based on a pointwise selection of small image patches of fixed size in the variable neighborhood of each pixel. Our contribution is to associate with each pixel the weighted sum of data points within an adaptive neighborhood, in a manner that it balances the accuracy of approximation and the stochastic error, at each spatial position. This method is general and can be applied under the assumption that there exists repetitive patterns in a local neighborhood of a point. By introducing spatial adaptivity, we extend the work earlier described by Buades et al. which can be considered as an extension of bilateral filtering to image patches. Finally, we propose a nearly parameter-free algorithm for image denoising. The method is applied to both artificially corrupted (white Gaussian noise) and real images and the performance is very close to, and in some cases even surpasses, that of the already published denoising methods.

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Algorithms Artifacts Computer Simulation Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Information Storage and Retrieval Models, Statistical Signal Processing, Computer-Assisted Stochastic Processes Journal Article

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Charles Kervrann, Jérôme Boulanger. 2006. Optimal spatial adaptation for patch-based image denoising.. https://doi.org/10.1109/tip.2006.877529

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