These are the most impressive results I'm aware of:
but both gives the best results with specialized training (faces for
example). The one Michael proposed seems a more generic one.
No idea about performance but "execution" time is usually fast, memory may
be a much bigger requirement.
This one is about noise reduction:
I think in a couple of years machine learning will be the only way to
achieve state of the art results for about anything. If not today already.
Noise, white balance, clipping recovery, blurry/out of focus shots, color
casts, lens correction, defringe, sharpening, inpainting, etc. Up to this
(pose edit, content generation):
their-applications (slide 24, 27)
It's going to be fun :)
2017-07-09 18:14 GMT+02:00 David Vincent-Jones <david...@gmail.com>:
> Along the same line: Some years ago Cliff Reiter (Lafayette College)
> (using Jsoftware) demonstrated and published 'lossless edge' image
> rotation using fractals.
> On 07/09/2017 08:16 AM, Michael Below wrote:
> > Hi,
> > last week I took a couple of images at a concert, and it turned out
> > that only a small part of each image was interesting. I was too far
> > away, with a wide-angle lens, so the band I wanted to photograph was in
> > a small part in the center of the frame with lots of other stuff around
> > them, stage, audience etc.
> > Now this can be solved by taking better pictures, coming closer, being
> > prepared with a telephoto lens etc. - but there also seems to be a
> > solution that could find its way into darktable.
> > There have been a number of media reports about machine learning
> > experiments by Google etc. to add missing detail to images during
> > upscaling. It seems like the results are often quite convincing. Now I
> > stumbled upon a Github project for this that seems to offer a hands-on
> > solution which might be a basis for implementation in darktable:
> > https://github.com/lucasdupin/ml-image-scaling
> > What do you think? I imagine this would be useful...
> > Cheers
> > Michael
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