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, its "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.
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This page was last edited on 10 March 2021, at 20:47