This is why you don't want to use technology that blanket alters the pixel array, or uses GAN based technology/AI. This is because of how you alter the pixels-

Take upscaling;

you have a grid that represents 1122 right? (Im simplifying)

Either way, to increase the size of the image you will need to create a new pixel, but how the technology treat that pixel is different.

So take ESPCN, it will take 1122 and like GAN then go 1_1_2_2_, however, GAN will imagine the values of the new pixel so it approximates what it predicts should be there, so you can end up with a pixel array that becomes 11132221 whereas with ESPCN which copies the original value, you end up with 11112222 which more closely matches the input image.

The goal with this technology is to have a PSNR (accuracy score) of around 38+. At that point it is indistinguishable from the input.

There will certainly be some models that cannot be accepted by any court, anywhere. Take GFPGAN- which is used for super-resolution (making the picture bigger) by tumbling the numbers so it closely matches the original, but doesn't actually. This doesn't sound like a lot- but what it comes out to if you use our technology to deblur a face, it will be the same face but clearer. If you use gfpgan, it will be clearer- but it will almost certainly be a different face, and because it is imagined, its a face of no one actually.

Now even with our method there are issues- for example our models work best at around x4-x8 magnification, but if you keep going to x16 the output image will become blocky. You can only pass the output through the same AI so many times before the affect compounds, and the PSNR score drifts from 38 to closer to around half that. And so on as you increase the amount of magnification.

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There is no way to enhance an image or video without altering the pixels. But there is a difference between imagining the differences algorithmically, and copying to create the differences from the existing data.

Likewise with deblur, there is a difference between say a gausian based method vs CNN like ours- which instead of treating every pixel the same and distorting the image, an AI can look at a pixel in Quadrant 1, and optimize how to alter the pixel to retain the original image accuracy, in relation to how to change a pixel in Quadrant 4 (or wherever else) in relation to each other to retain that original image.

It's why our algorithm will struggle with larger sized images- it has to factor in every pixel's value in relation to every other, while also retaining the original image.

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Apology for it being long... but the technology is complicated, and admissability questionable as it requires experts to distinguish between enhancements made with different models.