with some form of Approximate Nearest Neighbor method since the exhaustive search for the best pixel is somewhat slow. The synthesis can also be performed in multiresolution, such as through use of a noncausal nonparametric multiscale Markov random field. Patch-based texture synthesis Patch-based texture synthesis creates a new texture by copying and stitching together textures at various offsets, similar to the use of the clone tool to manually synthesize a texture. Image quilting and graphcut textures are the best known patch-based texture synthesis algorithms. These algorithms tend to be more effective and faster than pixel-based texture synthesis methods. Deep Learning and Neural Network Approaches 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 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. Another recent development is the use of generative models for texture synthesis. The Spatial GAN method showed for the first time the use of fully unsupervised GANs for texture synthesis. In a subsequent work, 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. In addition, flexible sampling in the noise space allows to create novel textures of potentially infinite output size, and smoothly transition between them. This makes PSGAN unique with respect to the types of images a texture synthesis method can create. Implementations Some texture synthesis implementations exist as plug-ins for the free image editor Gimp: Texturize Resynthesizer A pixel-based texture synthesis implementation: Parallel Controllable Texture Synthesis Patch-based texture synthesis: KUVA: Graphcut textures Deep Generative Texture Synthesis with PSGAN, implemented in Python with Lasagne + Theano: https://github.com/zalandoresearch/psgan Literature Several of the earliest and most referenced papers in this field include: Popat in 1993 - "Novel cluster-based probability model for texture synthesis, classification, and compression". Heeger-Bergen in 1995 - "Pyramid based texture analysis/synthesis". Paget-Longstaff in 1998 - "Texture synthesis via a noncausal nonparametric multiscale Markov random field" Efros-Leung in 1999 - "Texture Synthesis by Non-parametric Sampling". Wei-Levoy in 2000 - "Fast Texture Synthesis using Tree-structured Vector Quantization" although there was also earlier work on the subject, such as Gagalowicz and Song De Ma in 1986, "Model driven synthesis of natural textures for 3-D scenes", Lewis