very simple as only the mean and the variance on each feature dimension needs to be fitted.

While in the original work [@Reinhard:2001], the envisaged transferable element is color, later, the style of the image was approached [@bae2006]. Prior to popularization of CNNs, image descriptors were based on manually–set filter weights; a prominent is the case of the Laplacian style transfer [@Aubry2014]. In the last period, the spectacular results showed by Gatys et al. [-@Gatys:2015] helped the so called Neural Style Transfer methods to gain in popularity.

In the next subsections we will review the formalization of the main style transfer methods; $\mathbf{S}$ will denote the subject image, while $\mathbf{R}$ the style reference image. In some solutions, $\mathbf{S}$ is altered to mimic the style from $\mathbf{R}$, in others, a third image $\mathbf{X}$, is built from scratch to match the content from $\mathbf{S}$ and the style from $\mathbf{R}$.

### Laplacian style transfer

Following the findings of Sunkavalli et al. [-@sunkavalli2010], the complex transfer process produces better results if it is carried separately on different levels of details from the two images. The most simple form to have various details of an image is to use a pyramidal representation. The Laplacian style 