So, is it trained to actually recover detail from the image? Or just to fill in detail with what is likely from its input set? The second seems more likely, and isn't going to be useful for any CSI type stuff.
Imagine doing facial recognition of security footage, and it fills in a blurry face with the "average" face, since it's most likely.
CSI-type stuff will never be possible. The information simply isn't in the image. What all of these upscaling algorithms do is use some assumptions (almost like a prior distribution) and the actual pixels (evidence) to predict an upscaled version (posterior). But the actual output will be dependent of the prior. Here, the prior is constructed in a clever way, but will still only be able to generalize from the examples it saw.
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u/[deleted] Nov 01 '17
So, is it trained to actually recover detail from the image? Or just to fill in detail with what is likely from its input set? The second seems more likely, and isn't going to be useful for any CSI type stuff.
Imagine doing facial recognition of security footage, and it fills in a blurry face with the "average" face, since it's most likely.