Image Analogies
Inhaltsverzeichnis
Reference
Hertzmann, A., Jacobs, C., Oliver, N., Curless, B., Salesin, D. (2001). Image Analogies. In Fiume, E., ed.: Proceedings SIGGRAPH 2001. ACM Press, 327–340.
DOI
http://dx.doi.org/10.1145/383259.383295
Abstract
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.
Extended Abstract
Bibtex
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Links
Full Text
http://www.mrl.nyu.edu/publications/image-analogies/analogies-72dpi.pdf
