Image glossary
Super-resolution
Also known as AI upscaling, neural upscaling
Super-resolution is the use of a trained model to increase an image's resolution by generating plausible detail consistent with its training data, rather than by interpolating between existing pixels.
What it does differently
A super-resolution model is trained on millions of pairs: a high-resolution image and a degraded version of it. Over that corpus it learns the statistical relationship between what a downscaled eyelash looks like and what the eyelash looked like.
At inference it applies that learned mapping. Given forty pixels that look like a downscaled eye, it generates the several hundred pixels that a real eye would most plausibly have produced.
The key word is plausibly. The model is not recovering your detail. It is generating detail consistent with its training data, conditioned on the low-resolution evidence in your file.
Where that is fine and where it is not
On texture it is excellent. Skin, hair, fabric, foliage, stone, water — the eye judges these on whether the texture is plausible, not on whether individual elements are correct, and nobody can tell that leaf 4,182 is in the wrong place. Portraits are the strongest case because faces are the most heavily represented category in every training set.
On information it is a liability. Asked to enlarge illegible text, a model produces sharp, confident, fabricated letters — which is far worse than remaining blurry, because a crisp result invites you to read it. The same applies to serial numbers, licence plates, logos with specific letterforms, and any face that has to be identified rather than merely admired.
The question to ask first
Before running an upscale: would a plausible-but-different detail here cause a problem? A jumper's knit pattern invented differently is fine. A serial number invented differently is not. Leaves on a tree, fine. A face in a legal exhibit, not.
That single question resolves nearly every real case, and it reduces to a rule: texture is safe, information is not.
Practically: start from the best source you have rather than a compressed copy, prefer 2x over 4x where you can because more evidence per generated pixel means fewer inventions, do not run output back through the model, and check the result at 100% on any text, faces, and fine repeated patterns.
Tools that work with this
Related terms
- Bicubic interpolationBicubic interpolation is a resampling method that estimates each new pixel from a weighted cubic function of the sixteen surrounding source pixels, producing smoother results than simpler methods without adding any real detail.
- OCROCR is the process of detecting and transcribing text from an image into machine-readable characters.
- Raster vs vectorA raster image stores a fixed grid of pixels and degrades when enlarged; a vector image stores shapes as mathematical descriptions and renders sharply at any size.
- PPIPPI is the number of pixels per inch in an image or on a display, and it is the measurement people usually mean when they say DPI.
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