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Old 5th March 2018, 23:42   #41  |  Link
Asmodian
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At those prices buying a Titian V would seem a better investment.
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Old 21st March 2018, 20:42   #42  |  Link
Selur
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may be buying a Titan V wouldn't be such a good idea,... http://www.theregister.co.uk/2018/03...producibility/
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Old 2nd September 2018, 07:02   #43  |  Link
SSH4
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Sorry, may be not correct to post in this thread.
But found fresh commercial release of Topaz A.I. Megascale.
And looks like it finally that can be named modern A.I. upscale, that ready for consumers.

SRC


200% no enchance


400% with enchance
https://i.imgur.com/PoEIzan.jpg


SRC


200% no enchance


400% with enchance
https://i.imgur.com/Zd6EMVa.jpg


SRC


200% no enchance


400% with enchance
https://i.imgur.com/gVJa87X.jpg


SRC


200% no enchance


400% with enchance
https://i.imgur.com/3I2A99w.jpg


Good that it quite fast and have batch support.
Bad (may be) that this it work only with RGB.
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Old 2nd September 2018, 17:24   #44  |  Link
madshi
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It looks very nicely detailed and has a high-res look, but whether or not it has the same problems (e.g. create "ghost" images where the neural network paints distorted faces where they shouldn't be etc) like other SRGAN neural networks we don't know for sure. Furthermore, in the baby face, look at the eye lashes of the left eye. *Heavy* ringing artifacts there!
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Old 2nd September 2018, 19:26   #45  |  Link
davidhorman
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And the real question is: how well does it work with video?
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Old 2nd September 2018, 21:28   #46  |  Link
johnmeyer
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Originally Posted by madshi View Post
It looks very nicely detailed and has a high-res look, but whether or not it has the same problems (e.g. create "ghost" images where the neural network paints distorted faces where they shouldn't be etc) like other SRGAN neural networks we don't know for sure. Furthermore, in the baby face, look at the eye lashes of the left eye. *Heavy* ringing artifacts there!
I see ringing on the 200% pic in his right eye (his right, our left), but do NOT see those artifacts in the 400% version posted on Imgur. The 400% has "enhancement" and the 200% does not. However, I have no idea what enhancement is being applied and whether that would reduce ringing.
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Old 2nd September 2018, 23:05   #47  |  Link
davidhorman
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For what it's worth, here is the baby's eye from the 200% version (left) compared with the 400% version scaled down:



400% version is a lot cleaner and has a lot fewer JPEG artefacts (over the whole image).
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Old 3rd September 2018, 02:45   #48  |  Link
SSH4
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I guess that enhance mean some presharpen and some denoise.

With videos... well it have batch mode. just drag and drop and start.

More question about temporal stability for videos. And not least colors (as i see it work only with RGB)
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Old 3rd September 2018, 02:50   #49  |  Link
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Comparing to other upscalers. I think this one can be used for some video works. But it will required prefiltering (especially for DVD sources) and post filterings (like dehalo,"smart" sharpen, etc.) to rich best results.
So this is not "one click" Solution. but it looks more ready than most of "proof of concepts" on web. Because already have prebuild binaries (with not a bad UI) already optimized for memory (you can upscale 4K to 8K) etc.
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Old 9th September 2018, 19:32   #50  |  Link
foxyshadis
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Katie-derailment has been moved out to its own thread.
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Old 10th September 2018, 18:16   #51  |  Link
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sort of related and on topic - WolframRhodium has a bunch of pre trained models that can be used in vapoursynth with mxnet

https://github.com/WolframRhodium/Super-Resolution-Zoo
https://github.com/WolframRhodium/mu...tion_mxnet.vpy

nice to see implementations of various research papers
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Old 10th September 2018, 19:07   #52  |  Link
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sort of related and on topic - WolframRhodium has a bunch of pre trained models that can be used in vapoursynth with mxnet

nice to see implementations of various research papers
Those models are not implemented or even trained by me. I simply convert the models provided by the authors of academic papers or other algorithm engineers that are implemented in other deep learning framework to MXNet. You should attribute the main contribution to them rather than me.
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Old 10th September 2018, 19:14   #53  |  Link
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Those models are not implemented or even trained by me. I simply convert the models provided by the authors of academic papers or other algorithm engineers that are implemented in other deep learning framework to MXNet. You should attribute the main contribution to them rather than me.
Right, sorry bad phrasing .

But thanks for converting them to for MXNet compatibility. It's nice to be able to actually test some of these things out. It's extremely difficult to use some of the algorithms outlined in the various papers for typical users
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Old 10th September 2018, 21:08   #54  |  Link
Wilbert
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Katie-derailment has been moved out to its own thread.
I removed them and restored his/her other thread.
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Old 11th September 2018, 01:11   #55  |  Link
WolframRhodium
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Right, sorry bad phrasing .

But thanks for converting them to for MXNet compatibility. It's nice to be able to actually test some of these things out. It's extremely difficult to use some of the algorithms outlined in the various papers for typical users
Yes, some algorithms are more inclined to be benchmarks rather than for actual use. Just use the other efficient ones.
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