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An image analogy is a method of creating an image filter automatically from training data. In an image analogy process, the transformation between two images A and A' is "learned". Later, given a different image B, it's "analogy" image B' can be generated based on the learned transformation. The image analogy method has been used to simulate many types of image filters: * Toy filters, such as blurring or "embossing." * Texture synthesis from an example texture. * Super-resolution, inferring a high-resolution image 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, including oil, pastel, and pen-and-ink rendering, are synthesized based on scanned real-world examples. * Texture-by-numbers, in which realistic scenes, composed of a variety of textures, are created using a simple "painting" interface. * Image colorization, where color is automatically added to grayscale images. ==External links== * (Image Analogies at the New York University Media Research Lab ) 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Image analogy」の詳細全文を読む スポンサード リンク
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