Fast texture synthesis using tree, fast texture synthesis using tree-structured vector quantization
However, it remains difficult to design an algorithm that is both efficient and capable of generating high quality results.
Our algorithm is derived from Markov Random Field texture uses tree, and generates textures through a deterministic searching process. Conclusion can be found in Section 5. Wavelet theory has been successfully applied to many applications such as parameter estimation in fractal signals and images [ 23 — 29 ].
It generates textures with perceived quality equal to or better than those produced by previous techniques, but runs two orders of magnitude faster. 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.
This means multiple copies of the sample are simply copied and pasted side by side.
A 2-step matching, that is, coarse matching based on low-frequency wavelet packet coefficients followed by fine matching based on middle-high-frequency wavelet packet coefficients, is proposed for texture synthesis. In a subsequent synthesis,  the method was extended further—PSGAN can learn both periodic and non-periodic images in an unsupervised way from single images or large datasets of images.
One may easily tile small end of life essay to synthesize a larger image; however, there are some blocking effects near the tile edges [ 9 ]. Introduction Texture modeling can be effectively applied to a wide sulphonamide synthesis of natural surfaces such as plants, furs, skins, minerals, terrains, and fractal materials [ 12 ] and is an fast texture issue in cyber-physical systems [ 3 — 7 ].
Wei and Levoy took account of the order in which pixels were synthesized and proposed an order-independent search-based texture synthesis algorithm [ 12 ].