image analogies
This paper describes a new framework for processing images by example, called image analogies. The framework involves two stages: a design phase , in which a pair of images, with one image purported to be a filtered version of the other, is presented as training data; and an application phase , in which the learned filter is applied to some new target image in order to create an analogous filtered result. Image analogies are based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis. By choosing different types of source image pairs as input, the framework supports a wide variety of image filter effects, including traditional image filters , such as blurring or embossing; improved texture synthesis , in which some textures are synthesized with higher quality than by previous approaches; super-resolution , in which a higher-resolution image is inferred from a low-resolution source; texture transfer , in which images are texturized with some arbitrary source texture; artistic filters , in which various drawing and painting styles are synthesized based on scanned real-world examples; and texture-by-numbers , in which realistic scenes, composed of a variety of textures, are created using a simple painting interface.
imaging imaging analog paper process examples filtering version data order autoregression texture synthesis texture types input effect quality texture transfer drawing paint scan texture-by-numbers interfaces  framework  scale markov random fields non-photorealistic rendering example-based rendering image