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:
“on the instant clamorous eaves,
A climbing moon upon an empty sky,
And all that lamentation of the leaves,
Could but compose man’s image and his cry.”
—William Butler Yeats (1865–1939)
“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)