Image Denoising with Kernels Based on Natural Image Relations
Valero Laparra, Juan Gutiérrez, Gustavo Camps-Valls, Jesús Malo; 11(29):873−903, 2010.
A successful class of image denoising methods is based on Bayesian approaches working in
wavelet representations. The performance of these methods improves when relations among the local frequency coefficients are explicitly included. However, in these techniques, analytical
estimates can be obtained only for particular combinations of analytical models
of signal and noise, thus precluding its straightforward extension to deal with other arbitrary noise sources.
In this paper, we propose an alternative non-explicit way to take into account the relations among natural image wavelet coefficients for denoising: we use support vector regression (SVR) in the wavelet domain to enforce these relations in the estimated signal. Since relations among the coefficients are specific to the signal, the regularization property of SVR is exploited to remove the noise, which does not share this feature. The specific signal relations are encoded in an anisotropic kernel obtained from mutual information measures computed on a representative image database. In the proposed scheme, training considers minimizing the Kullback-Leibler divergence (KLD) between the estimated and actual probability functions (or histograms) of signal and noise in order to enforce similarity up to the higher (computationally estimable) order. Due to its non-parametric nature, the method can eventually cope with different noise sources without the need of an explicit re-formulation, as it is strictly necessary under parametric Bayesian formalisms.
Results under several noise levels and noise sources show that: (1) the proposed method outperforms conventional wavelet methods that assume coefficient independence, (2) it is similar to state-of-the-art methods that do explicitly include these relations when the noise source is Gaussian, and (3) it gives better numerical and visual performance when more complex, realistic noise sources are considered. Therefore, the proposed machine learning approach can be seen as a more flexible (model-free) alternative to the explicit description of wavelet coefficient relations for image denoising.
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