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-resolutinon 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.
Famous quotes containing the words image and/or analogy:
“Her image was my ensign: snows melted,
Hedges sprouted, the moon tenderly shone,
The owls trilled with tongues of nightingale.”
—Robert Graves (1895–1985)
“The analogy between the mind and a computer fails for many reasons. The brain is constructed by principles that assure diversity and degeneracy. Unlike a computer, it has no replicative memory. It is historical and value driven. It forms categories by internal criteria and by constraints acting at many scales, not by means of a syntactically constructed program. The world with which the brain interacts is not unequivocally made up of classical categories.”
—Gerald M. Edelman (b. 1928)