Instead of training on vast amounts of arbitrary data that may lead to hallucinations, wouldn't it be better to train on high-resolution images of the specific subject we want to upscale? For example, using high-resolution modern photos of a building to enhance an old photo of the same building, or using a family album of a person to upscale an old image of that person. Does such an approach exist?
Author here -- Generally in single image super-resolution, we want to learn a prior over natural high-resolution images, and for that a large and diverse training set is beneficial. Your suggestion sounds interesting, though it's more reminiscent of multi image super-resolution, where additional images contribute additional information, that has to be registered appropriately. That said, our approach is actually trai…
Re: Arbitrary-Scale Super-Resolution with Neural Heat Fields
#31I'm not a data scientist, but I assume that having more information about the subject would yield better results. In particular, upscaling faces doesn't produce convincing outcomes; the results tend to look eerie and uncanny.
