What you really want is deep nets trained for specific sensors/ISO combinations
for denoising. Looks like neat doesn't use deep learning (if they would,
I'm sure they'd mention it in their explanation).
So it's unlikely to be state of the art (last time I checked).

On 07/10/2017 12:05 PM, Tim Rolph wrote:
Hi, if I have to upscale an image I usually use Reshade from www.reshade.com
it is a free app the works very well although its only for windows but works
fine under wine. Oh and its also free. If I ever have a problem with noise I
use Neat Image https://ni.neatvideo.com/ they have a free linux version that
is limited to save only in jpg but has support for opencl / cuda GPU
processing and it gives unbelievably good results.

Tim.

On Sunday, 9 July 2017 17:16:12 BST 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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