English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A successful class of image denoising methods is based on Bayesian approachesworking in wavelet representations. However, analytical estimates can beobtained only for particular combinations of analytical models of signal andnoise, thus precluding its straightforward extension to deal with otherarbitrary noise sources. In this paper, we propose an alternative non-explicitway to take into account the relations among natural image wavelet coefficientsfor denoising: we use support vector regression (SVR) in the wavelet domain toenforce these relations in the estimated signal. Since relations among thecoefficients are specific to the signal, the regularization property of SVR isexploited to remove the noise, which does not share this feature. The specificsignal relations are encoded in an anisotropic kernel obtained from mutualinformation measures computed on a representative image database. Trainingconsiders minimizing the Kullback-Leibler divergence (KLD) between theestimated and actual probability functions of signal and noise in order toenforce similarity. Due to its non-parametric nature, the method can eventuallycope with different noise sources without the need of an explicitre-formulation, as it is strictly necessary under parametric Bayesianformalisms. Results under several noise levels and noise sources show that: (1)the proposed method outperforms conventional wavelet methods that assumecoefficient independence, (2) it is similar to state-of-the-art methods that doexplicitly include these relations when the noise source is Gaussian, and (3)it gives better numerical and visual performance when more complex, realisticnoise sources are considered. Therefore, the proposed machine learning approachcan be seen as a more flexible (model-free) alternative to the explicitdescription of wavelet coefficient relations for image denoising.

Image Denoising with Kernels based on Natural Image Relations