ABSTRACT
In a relatively similar context, kernels have also been employed by other denoising methods that are not considered here. For example, in [14] and [35] a support vector regression approach is considered for the Gaussian noise case, while in [12] the kernel principal components of an image are extracted and this expansion is truncated to produce the denoising effect.
