VSGAN - VapourSynth GAN Implementation, based on ESRGAN's implementation

[kedautinh12]

Too clean and after use it, i will add some noise to match my favourite

[ChaosKing]

You could merge some % of the original clip back to add back some texture.
I just like the overall consistency of the denoising result.

examples of how clean it gets:
https://imgur.com/a/UOjtk8Y
https://imgur.com/a/HB02pCW
https://imgur.com/a/6mQuyIn

[lansing]

Originally Posted by ChaosKing

New toy https://github.com/HolyWu/vs-scunet
Online demo https://replicate.com/cszn/scunet

So far I like it. It creates very consistent and clean results (a bit too clean).

Tested on my anime, the denoising is pretty good. There are some aggressive sharpening when it determined something is an blurred edges. The noise sensitivity is also too high, in my anime here it mistaken the pattern on the wall to be noise.

https://imgsli.com/MTQ0MTkz

[zorr]

Originally Posted by ChaosKing

New toy https://github.com/HolyWu/vs-scunet
Online demo https://replicate.com/cszn/scunet

So far I like it. It creates very consistent and clean results (a bit too clean).

This looks like an excellent prefilter to MVTools, especially for very noisy videos.

I tried to install this to my portable VapourSynth but it looks like vsscunet wants at least Python 3.10 and the latest FatPack is still at 3.9. Are you planning a new release?

[poisondeathray]

scunet is good on gaussian type noise (the pretrained models were trained on synthetic gaussian noise)

non anime example
https://imgsli.com/MTQ0MTk3/0/0

It would be nice if the psnr model had adjustable strength built in.

The gan model retains a bit more detail compared to psnr model; the gan model exhibits less denoising but shifts the colors a bit more

[ChaosKing]

Originally Posted by zorr

This looks like an excellent prefilter to MVTools, especially for very noisy videos.

I was thinking the same

Originally Posted by zorr

I tried to install this to my portable VapourSynth but it looks like vsscunet wants at least Python 3.10 and the latest FatPack is still at 3.9. Are you planning a new release?

I was actually planning to release a GPU Edition but things got in the way and some laziness. I hope this month

Originally Posted by poisondeathray

The gan model retains a bit more detail compared to psnr model; the gan model exhibits less denoising but shifts the colors a bit more

yeah, color shifts are the most annoying thing with these trained filters.

[lollo2]

I used one of my S-VHS capture (affected by ghosting and halo) with typical noise and the result is very good: deep denoise, but not too much loss of details.
However, I still prefere a classic AviSynth TemporalDegrain2 + LSFmod flow.

Comparison between original and AviSynth: https://imgsli.com/MTI5NjI2

Comparison between original and Scunet: https://imgsli.com/MTQ0MTgw

Comparison between AviSynth and Scunet: https://imgsli.com/MTQ0MTgx

I will try to play with the Scunet options to match AviSynth more "natural" look.

The processing time is very high, >1 minute for a single frame, I hope it will not be like that on a local machine

[Selur]

Nice.
I agree, looks nice, but it would be nice to have some more control over the denoising strength.
(dot crawlers, light blocking get removed nicely, but general denoising and smoothing seems too strong, probably best applied with some masking)

[zorr]

Originally Posted by Selur

I agree, looks nice, but it would be nice to have some more control over the denoising strength.

The strength can be somewhat controlled with a couple of tricks. The first one is to amplify the dark areas with gamma (in RGB space) before the denoising and then undoing the gamma change afterwards. It makes the noise look more like actual features so the denoiser leaves more of it intact. The effect is quite subtle though. And it seems to amplify the color drift scunet is doing.

gamma = 1.5
clip = core.std.Levels(clip, gamma=gamma, planes=[0,1,2])
clip = scunet(clip, model = 4)
clip = core.std.Levels(clip, gamma=1.0/gamma, planes=[0,1,2])

Another and more effective way is to upscale the clip before the denoising and rescaling it back to original size afterwards. Here for example with scaling factor 2:

orig_width = clip.width
orig_height = clip.height
clip = core.resize.Lanczos(clip, width=clip.width*2, height=clip.height*2, src_left = -0.25, src_top = -0.25)
clip = scunet(clip, model = 4)
clip = core.resize.Point(clip, width=orig_width, height=orig_height)

Obviously this takes 4 times as much memory and processing power. Scaling by 2 leaves quite a lot more noise so perhaps a smaller scale factor will be enough. And you can of course combine this with the gamma trick.

[lansing]

I'm trying to create a lower quality image from a higher quality image for training a model for anime. I need to recreate a dvd quality image from a cel scan,

from this:
https://imgur.com/FWW2p6B

to this:
https://imgur.com/VdrndTA

Do we have vapoursynth filters that can do that?

[mastrboy]

https://gitlab.com/bg123/vapoursynth.../-/tree/master (I have not tested it)

[lansing]

Originally Posted by mastrboy

https://gitlab.com/bg123/vapoursynth.../-/tree/master (I have not tested it)

The syntax was outdated? I'm getting "BlankClip: invalid length" error on this line:

attribute_clip = core.std.BlankClip(clip, length=math.floor(len(clip) * target_fps_num / target_fps_den * clip.fps_den / clip.fps_num), fpsnum=target_fps_num, fpsden=target_fps_den)

[ChaosKing]

Almost perfect decrawl / Derainbow filter 1x_Dotzilla_Compact. https://upscale.wiki/wiki/Model_Data...e_and_Cartoons
It just works + speed is also ok.

https://imgsli.com/MTM4ODkz
https://i.imgur.com/hp6tBMC.jpeg

[Selur]

@ChaosKing: nice.

[ReinerSchweinlin]

Originally Posted by ChaosKing

Almost perfect decrawl / Derainbow filter 1x_Dotzilla_Compact. https://upscale.wiki/wiki/Model_Data...e_and_Cartoons
It just works + speed is also ok.

https://imgsli.com/MTM4ODkz
https://i.imgur.com/hp6tBMC.jpeg

looking good

[PatchWorKs]

Originally Posted by ChaosKing

New toy https://github.com/HolyWu/vs-scunet
Online demo https://replicate.com/cszn/scunet

So far I like it. It creates very consistent and clean results (a bit too clean).

Nice, here's a "degrain" of a 1080p source (model - real image denoise):



Definition and shadows looks ok, blurry is minimal.

Anyway I can notice a little banding (check the sky in the center of the image).

EDIT: https://imgsli.com/MTUxMTY5 (overlay comparison)

[ChaosKing]

yeah its very good. But look at the guys shirt. It's like a "shadow" is removed or the shirt got wasched clean. Interessting effect

I wonder if a "temporal scunet" would be even better.

If think if you combine the scunet filtered frame with a smdegrain filtered frame the result would be almost perfect (maybe add some debanding too)

[PatchWorKs]

...it would be also interesting to have a "combined" (denoise+SDRtoHDR) tool:

https://github.com/MCG-NKU/FMNet

It could be also *very* useful to perform a block partitioning-optimization at the same time:

https://github.com/fengxinmin/iDepthmap-x265

[PatchWorKs]

Just discovered aydin, "a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms":




Website: https://royerlab.github.io/aydin/

Git: https://github.com/royerlab/aydin

Quite surprised that we didn't found any related search result here ad Doom9...

[Selur]

Not that surprising, since there are not many threads that are image centric.
Usually it's about video. And image based filters can easily lead to temporal inconsistencies.