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It's information theory. No matter what blur technique you use, there's only so much information in the pixels. You can't produce more information by magic, o

by not2b 6y ago

It's information theory. No matter what blur technique you use, there's only so much information in the pixels. You can't produce more information by magic, or by technology so advanced that it is indistinguishable from magic (h/t Arthur Clarke).

tyingq 6y ago
Well, I'm suggesting that, for example, pixelation where you change 10x10 blocks of pixels to 1 10x10 block of a single color...is very lossy. A blur of a known algorithm (gaussian, for example), can be somewhat reversed. It's not as lossy.
- WrtCdEvrydy 6y ago
  Best blur method is just to make all pixels black.
- qayxc 6y ago
  You seem to have misunderstood what the article is about. It's not about turning a shitty image to a nice a clean one. It's about turning a lowres image into a hires version, i.e. you start with 100 x 100 pixels (doesn't matter how you obtained them - could be a section of a much bigger image, for example) and try to extrapolate a 1k x 1k pixel version from it. The pixelation you see in the examples is just a representation of what little information you have to work with. It's NOT in any way shape or form related to where you get these pixels in from in the first place. Just to give you a different context here: imagine this upscaling being used to "enhance" a single face in an image like this [1] - there's no way "Gaussian blur" or whatever filter you'd like gets you more information out of that. In the example image I created, I applied a Gaussian blur to the "pixelated" (i.e. enlarged) version of the marked image section. As you can see, enlarging the same section using super-sampling (i.e. similar to what you proposed), doesn't change the information content and one version can basically be transformed into the other. [1] https://imgur.com/a/m23u6Xx https://imgur.com/a/m23u6Xx
  - tyingq 6y ago
    No, I understood. The article gives several different examples of "blurry", and they aren't all the same. Fixing motion blur is one example of something where you aren't just completely guessing and "inpainting".

by not2b 6y ago

tyingq 6y ago

Well, I'm suggesting that, for example, pixelation where you change 10x10 blocks of pixels to 1 10x10 block of a single color...is very lossy. A blur of a known algorithm (gaussian, for example), can be somewhat reversed. It's not as lossy.

WrtCdEvrydy 6y ago
Best blur method is just to make all pixels black.
qayxc 6y ago
You seem to have misunderstood what the article is about. It's not about turning a shitty image to a nice a clean one. It's about turning a lowres image into a hires version, i.e. you start with 100 x 100 pixels (doesn't matter how you obtained them - could be a section of a much bigger image, for example) and try to extrapolate a 1k x 1k pixel version from it. The pixelation you see in the examples is just a representation of what little information you have to work with. It's NOT in any way shape or form related to where you get these pixels in from in the first place. Just to give you a different context here: imagine this upscaling being used to "enhance" a single face in an image like this [1] - there's no way "Gaussian blur" or whatever filter you'd like gets you more information out of that. In the example image I created, I applied a Gaussian blur to the "pixelated" (i.e. enlarged) version of the marked image section. As you can see, enlarging the same section using super-sampling (i.e. similar to what you proposed), doesn't change the information content and one version can basically be transformed into the other. [1] https://imgur.com/a/m23u6Xx https://imgur.com/a/m23u6Xx
- tyingq 6y ago
  No, I understood. The article gives several different examples of "blurry", and they aren't all the same. Fixing motion blur is one example of something where you aren't just completely guessing and "inpainting".