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When I was still in computer vision, "super-resolution" meant taking multiple images and combining them to really be able to see what was in the scene. Now it
by petters 10y ago
When I was still in computer vision, "super-resolution" meant taking multiple images and combining them to really be able to see what was in the scene.
Now it often means: take a single image and have the computer guess/make up image content. Not the same at all, IMO. There are worse examples than this, though.
- BugsJustFindMe 10y agoI don't know when you were in computer vision, but when I was in computer vision a bit more than a decade ago it meant both.
- yousry 10y agoI would also call it a perverted use of the term super-resolution. NNs perform image manipulations beyond resolution enhancement. They add features based on trained sources.
- BugsJustFindMe 10y agoAnd? Do a search on scholar for "Example-based Super Resolution" or "Markov Networks for Superresolution". The term has been used this way in literature for maybe 20 years now.
- petters 10y agoOK. In my (limited) view of the field these kind of things started with "Super-Resolution From a Single Image" in 2009 but apparently it is older than that. To me personally, it feels weird to call this super-resolution, but I realize I may be in the minority.
- posterboy 10y agoIsn't either essentially the same. To say guessing neural networks combine the input of multiple images would be an understatement.
- ISL 10y agoOne is attempting to ascertain an underlying commonality given multiple measurements, the other is attempting to infer more information than is present in any measurement. They're quite different.
- petters 10y agoHave you seen the "super-resolution" results with faces and NN? Imagine grainy CCTV footage as evidence in court. A serious image analyst may attempt using super-resolution to obtain the face of a suspect. But a NN could happily insert a very plausible face that looks great. It could have little to do with reality, though.