Texture synthesis

These algorithms perform well with stochastic textures only, otherwise they produce completely unsatisfactory results as they ignore any kind of structure within the sample image.

Algorithms of that family use a fixed procedure to create an output image, i. e. they are limited to a single kind of structured texture.

This method, proposed by the Microsoft group for internet graphics, is a refined version of tiling and performs the following three steps: The result is an acceptable texture image, which is not too repetitive and does not contain too many artifacts.

These methods, using Markov fields,[3] non-parametric sampling,[4] tree-structured vector quantization[5] and image analogies[6] are some of the simplest and most successful general texture synthesis algorithms.

More recently, deep learning methods were shown to be a powerful, fast and data-driven, parametric approach to texture synthesis.

The work of Leon Gatys[10] is a milestone: he and his co-authors showed that filters from a discriminatively trained deep neural network can be used as effective parametric image descriptors, leading to a novel texture synthesis method.

In addition, flexible sampling in the noise space allows to create novel textures of potentially infinite output size, and smoothly transition between them.