  In this work we propose a photorealistic style transfer method for image and
video that is based on vision science principles and on a recent mathematical
formulation for the deterministic decoupling of sample statistics. The novel
aspects of our approach include matching decoupled moments of higher order than
in common style transfer approaches, and matching a descriptor of the power
spectrum so as to characterize and transfer diffusion effects between source
and target, which is something that has not been considered before in the
literature. The results are of high visual quality, without spatio-temporal
artifacts, and validation tests in the form of observer preference experiments
show that our method compares very well with the state-of-the-art. The
computational complexity of the algorithm is low, and we propose a numerical
implementation that is amenable for real-time video application. Finally,
another contribution of our work is to point out that current deep learning
approaches for photorealistic style transfer don't really achieve
photorealistic quality outside of limited examples, because the results too
often show unacceptable visual artifacts.
