This model corrects jagged lines on animation that has been deinterlaced. It handles simple line doubling, line interpolation, and even Yadif-style artifacts. It can also handle sources that were resized after deinterlacing, for example resizing from ntsc to pal resolutions. If a source has been upscaled after deinterlacing, it will need to be downsized before applying this model.

AnimeUndeint Compact

Pretrained: 4xPSNR

Texture retaining denoiser and sharpener for digital artwork

Sample images: https://1drv.ms/u/s!Aip-EMByJHY28xKFp93VIGydNsoN?e=vN2WYh

ArtClarity

BCGone Detailed V2 40-60

Attempts to remove the damages done by BC1 compression.

BCGone Smooth

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux 720p and 1080p, but only if the noise is monochrome or close to it. So far, attempts are underway to create a dataset that could eliminate color noise at the same speed.

1x BW Denoise

Attempts to remove the damages done by color banding. For this model, I downscaled all of the HR images to make their pixel size closer to 1000x1000 using the Box filter and then downscaled them again by 50% using the Point filter. Afterwards, I applied 64-bit color banding to every image in the dataset.

Comparisons with other models: https://imgsli.com/NTA1NTk

Bandage Smooth

A model trained with the goal of preserving texture while removing halftones. I do plan on making improved versions down the line, as this one didn't come out as well as I'd like.

Bendel_Halftone

This model helps repair color bleed and heavy chroma noise that may be present on some older footage, particularly that which was recorded on VHS. It also cleans up rainbows if they are present.

BleedOut Compact

This is SaurusX's original description:
Improvement of low-quality cartoons from broadcast sources. Will greatly increase the visual quality of bad broadcast tape sources of '80s and '90s cartoons (e.g. Garfield and Friends, Heathcliff, DuckTales, etc). Directly addresses chroma blur, dot crawl, and rainbowing. You're highly advised to take care of haloing beforehand in your favorite video editor as the model will not fix it and may make existing halos more noticeable.

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Sadly this model has some intense artifacts. Thankfully the SPAN re-train reduced these a bit, but they're still problematic. This was just a quick test of SPAN's capabilities. This was trained only to ~66k iterations, compared to the 480k of the original.

BroadcastToStudio_SPAN

Halo Removal + edge removal. This model softens edges without blurring other parts of the image.

Comparison and use case: https://imgsli.com/MjgxMDU

DeEdge

[Link to Github Release](https://github.com/Phhofm/models/releases/tag/1xDeNoise_realplksr_otf)

# 1xDeNoise_realplksr_otf  
Scale: 1  
Architecture: [RealPLKSR](https://github.com/muslll/neosr/?tab=readme-ov-file#supported-archs)  
Architecture Option: [realplksr](https://github.com/muslll/neosr/blob/master/neosr/archs/realplksr_arch.py)  

Author: Philip Hofmann  
License: CC-BY-0.4  
Purpose: Restoration, Denoise    
Subject: Photography  
Input Type: Images  
Release Date: 08.08.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): Yes  
Pretrained Model: [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf)  
Iterations: 200'000  
Batch Size: 8  
Patch Size: 64  

Description:   
A 1x realplksr model to denoise, trained with the realesrgan-otf pipeline, also handles a bit of jpg compression (if stronger jpg compression handling is needed, [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf) can be used).

1xDeNoise_realplksr_otf 

Debandurh_FS Ultra-lite

DitherDeleter Smooth

Attempts to remove the damages done by dithering. For this model, I downscaled all of the HR images to make their pixel size closer to 1000x1000 using the Box filter and then downscaled them again by 50% using the Point filter. Afterwards, I applied 32-bit Riemersma to every image in the dataset.

https://imgsli.com/NTA5MjU/0/1

DitherDeleterV3 Smooth

Film-Degrainer_1-000

ISO denoise v1

ISO denoise v2

Removes banding, blocking, dithering, aliasing, noise and color tint on DDS Compressed Skin Diffuse Textures.

This should work extremely well on most modern DDS compression types. The training set was compressed with BC3/DXT5, BC3/DXT5 Fast, BC2/DXT3, BC2/DXT3 Fast, and a small number of JPEG compressed images to cover outliers.

This model is trained to remove the slight green color tint that DDS compression tends to add to skin textures, so the model output will not match the original color tone of the input image. This is the desired result though, as DDS compression shifts the colors to a sickly green tint and this model corrects that to more natural color tones.

The training set included faces, body parts, eyes, mouths and hair in a variety of skin types and tones so it should work well on most related diffuse textures.

However it's not just limited to skin, many other images and textures can be cleaned with this model. Designed to be used as a first step cleaning pass before applying additional models after. Check out the other ITF Models.

ITF SkinDiffDDS v1

NoiseToner Poisson

Attempts to remove the damages done from noise. Successor of sorts to Noisetoner_Poisson_150000_G

NoiseToner Poisson Detailed

NoiseToner Poisson Soft

NoiseToner Uniform

NoiseToner Uniform Detailed

NoiseToner Uniform Soft

A fast upscaler that is a replacement for standard methods of cleaning noise from the picture. Ideal for BDRemux or WEB-DL 720p or 1080p it. It gets rid of floral noise well, the smaller its grain size, the more uniform the resolution for all videos will be. If you need a more stable video, then you can also add 2x-DigitalFlim-SuperUltraCompact after this upscaler. My upscaler can slightly simplify the geometry of very small objects.

Architecture	ESRGAN
Scale	1x
Size	64nf23nb
Color Mode	RGB
License	WTFPL Private use Commercial use Distribution Modifications Disclaimer
Date	2021-10-08
Dataset	Realistic images
Dataset size	657
Training iterations	108000
Training epochs	658
Training batch size	4
Training HR size	64
Training OTF	No

NoiseToner Poisson Detailed

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