Image analogies

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Image analogies (2001)

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by Aaron Hertzmann

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455 - 8 self

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@INPROCEEDINGS{Hertzmann01imageanalogies,
    author = {Aaron Hertzmann},
    title = {Image analogies},
    booktitle = {},
    year = {2001},
    pages = {327--340}
}

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Abstract

Figure 1 An image analogy. Our problem is to compute a new “analogous ” image B ′ that relates to B in “the same way ” as A ′ relates to A. Here, A, A ′ , and B are inputs to our algorithm, and B ′ is the output. The full-size images are shown in Figures 10 and 11. 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 multiscale 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.

Keyphrases

image analogy texture synthesis arbitrary source texture new analogous image various drawing texture transfer filtered version scanned real-world example artistic filter new framework new target image learned filter source image pair application phase higher-resolution image image filter effect full-size image training data traditional image filter painting style design phase recent result realistic scene simple painting interface analogous filtered result different type low-resolution source wide variety previous approach simple multiscale autoregression