What a fun idea to play with, ZL181!
I have access to numerous machine learning upscaling programs between my work and my long-standing love for photography, so had to hop in and play around with this a little bit. One neat thing to consider is that a more advanced program for this sort of thing may have modes and configuration options which you can use to define parameters based on what you are working with. A low-resolution image with a lot of compression? Artwork? Line drawings? Photography? Product photography and architecture? So options like that can help a lot to decide how true you want to be to the source artwork, or how much you might want to depart.
In this case I used Topaz Gigapixel. It’s one of the leading programs for this sort of thing. There are now similar capacities baked into a lot of programs, although sometimes they are purpose-built for the use of that program (e.g. for photography).
I didn’t upload them here. I uploaded them to another website and linked them, and controlled the max side using the
tag I set up for this forum by request a short time ago. You can open the images in a new window to view them in full resolution. But sorry for the file sizes if anyone here is still using a slow connection.
One fun thing is that, with settings that were generally well-behaved across a dataset, it would be possible to create a high-resolution version of an entire collection of portraits.
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Guan Yu (RTK VIII)
Here is an upscale in a mode which I have set up to hold fairly close to the original piece. It really ends up bringing out the solid, heavy lines and delineations. It’s kind of funny to see how badly this program fails on the gold bit on his headpiece. It has no idea what it is. Because it is machine learning based, it is trying to make heads or tails of it based on its training.
This one is fun. This upscaling mode is focused on photography and the like, and it understands faces. It recognizes Guan Yu’s face and is making choices (in solving the “ill-posed problem” part of upscaling) based on what it understands of human faces and anatomy. Without hesitation it departs from the original art in doing this, other than honoring quite precisely dimensions and separation, and starts to introduce things like a transition from less hair growth at the top of the beard into thinker hair on down. It has also gone out of its way to expand the fabric of his clothing into something that looks quite impressive, and has tried to flesh out the detail in the clothing around his neck.
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Pretty Pretty Pei (RTK XIII)
This time I focused the upscaling on remaining reasonably true to the source piece. The standard modes which start to incorporate some knowledge of faces, hair, clothing seem to do well on these newer portraits, as they have moved a bit more into realistic depictions and textures. There is very little going on, here, which feels like a clear failure. One thing I do notice is the stronger texture and separation on the band of his leather armor where it is catching highlights. This is a common upscaling artifact. Because the separation of lights and darks is so much stronger in the original artwork, this ends up being amplified in upscaling in a manner that feels a bit removed.
A crop on his splendor.
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Jinhuan Sanjie (RTK XIII)
This upscaling is just legendary. The art style, here, just feeds in so nicely to what the machine learning understands. And there are numerous fun textures for it to play with, from teeth, hair, clothing, fur, skin, metal, and it really highlights what these programs can do.
It was possible to upscale this one pretty considerably without things materially breaking down. And it is just such a delight. Really makes me chuckle. You could make a really amazing (lol!) wallpaper out of this one.