Abstract
Here we propose
an alternative non-explicit way to take into account the relations among
wavelet coefficients in natural images for denoising: we use Support
Vector Machines (SVM) to learn these relations. Since relations among
the coefficients are specific to the signal, SVM regularization removes
the noise, which does not share this property. Moreover, due to its
non-parametric nature, the method can eventually cope with different
noise sources. The results show that: (1) the proposed non-parametric
method outperforms conventional methods that assume coefficient independence,
and (2) its performance is similar to state-of-the-art parametric
methods that do explicitly include these relations. Therefore, the
proposed machine learning approach can be seen as a more flexible
(model-free) alternative to the explicit description of wavelet coefficient
relations in Bayesian approaches.
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