On Mon, 2019-01-21 at 19:23 +0530, Maitraya Bhattacharyya via gimp-
developer-list wrote:
[...]
> Now to train these neural nets, I will have to prepare a dataset
> consisting
> of low resolution and high resolution images and divide them into
> training,
> testing and validation sets.
If it
Hi Elle,
I looked at the posts on discuss.pixls.us, and it seems that their approach
is quite different to what we are trying to achieve. They are mostly trying
to use multiple images or use traditional methods which don't yield very
good results.
Thanks,
Maitraya.
On Mon, Jan 21, 2019 at 9:12
On 1/21/19 10:01 AM, Elle Stone wrote:
I don't know if this is the same "super resolution", but FWIW the topic
has come up several times on discuss.pixls.us:
https://discuss.pixls.us/search?q=%20superresolution
My apologies, I wasn't very clear. discuss.pixls.us is a discussion
forum for
Hi Martin,
Thanks for the second link on Tensorlayer. if the license of the library is
compatible with GPL, there is no reason we cannot used their VGG19 network
but I don't think it is! I will still need a few weeks to create the image
dataset and then train this.
Thanks,
Maitraya.
On Mon,
Hi,
I already tried the first one, but it has not the quality I expected. Did
anybody tried the second one? The second one looks quite good :)
[1] https://github.com/alexjc/neural-enhance
[2] https://github.com/tensorlayer/srgan
Best regards,
Martin
Am Mo., 21. Jan. 2019 um 16:07 Uhr schrieb
On 1/21/19 8:53 AM, Maitraya Bhattacharyya via gimp-developer-list wrote:
super resolution
I don't know if this is the same "super resolution", but FWIW the topic
has come up several times on discuss.pixls.us:
https://discuss.pixls.us/search?q=%20superresolution
Best regards,
Elle Stone
Hi guys,
I think Casey's suggestion of super resolution is an excellent idea and
would be useful to a lot of people using GIMP. The two algorithms which
have been doing quite well are:
1. SRGAN: The relevant paper is at https://arxiv.org/abs/1609.04802 . This
has yielded some very nice results
