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

Attempts to remove the damages done by BC1 compression.

BCGone Smooth

Category: Luminance/Chroma

ChromaSharpen - makes the colors slightly more vibrant with a sideeffect of possibly adding Chromatic Abberation to the image. I am not sure about the usage of this model on real case scenarios but anything blurry or with fuzzy colors could work

BSChroma

Category: Luminance/Chroma
Purpose: Luma

Fix Luminance issues?? LumaSharpen ESRGAN Edition? - This model mostly does what "Lumasharpen" algorithms do. It may help fixing images with Luminance images issues? Like old DVD rips? I am not sure, didn't test.

BSLuma

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

DeBLR

Pretrained model: 1x_DeJpeg_Fatality_01_175000_G.pth

Fatality DeBlur

These models deblur most images. It was trained mostly on aniso2 and iso blurring (BSRGAN augmentation) with some gaussian mixed in. It performs well on most blurry images, but I'd recommend using something like Fatality_Deblur for very strong gaussian blur.

Focus

Focus Moderate

This is a sharpening/deblurring model for anime video. It was created with three goals in mind:
Be blazing fast
Try to avoid enhancing noise and compression artifacts
Try to avoid over-enhancing intentionally blurred parts of the image

Despite that last point, this is not intended to be used on modern anime which makes heavy of use depth-of-field effects.

HurrDeblur SuperUltraCompact

Category: Skin
Purpose: Skin Upscaling

Adds plausible high frequency detail and removes subtle blur. This is an early unfinished attempt at a x1 Lite model designed specifically for enhancing detail on skin diffuse textures of 3d characters. Even in its current state it works quite well.

Best suited for uncompressed or cleaned textures - otherwise it may just enhance any existing compression artefacts too. The training set included faces, body parts, eyes 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 enhanced with this model. The results are subtle, so run multiple times if desired.

Pretrained model: 50/50 Interpolation of DIV2K-Lite and SpongeBC1-Lite

ITF SkinDiffDetail Lite v1

This model is meant to reduce or eliminate JPEG Compression without making the original images too smooth or killing detail. It manages to do a fairly good job but don't expect overly compressed images to work with this.

Pretrained model: 1xESRGAN + previous attempts

JPEGDestroyer

PixelSharpen

DeBlur, ReFocus, Sharpen Manga, Anime and cartoon style images, but will work on real life images too.

ReFocus Cleanly

DeBlur, ReFocus, Sharpen Real life style images, but will work on Anime images too.

ReFocus V3

Sharpening, line darkening and slight line thinning, specifically made for the Boruto anime.

ThePi7on Solidd Deborutify UltraLite

Fix images that have been arbitrarily / poorly resized, such as non-integer nearest-neighbor upscaling/downscaling - Also acts as an image sharpener/deblur when used on slightly soft inputs

Pretrained model: 1x_UnResize_MKII_030000_G.pth

UnResize V3

mdeblur

In addition to removing the vertical blur, the model upscales, sharpens and will remove MPEG-2 artifacting and a small amount of rainbowing and dot crawl. Another series afflicted with the vertical blur is Avatar the Last Airbender, which can be repaired by this model. The video fed into the model MUST be 540 vertical for the deblur to work properly.

SwatKats

After not being entirely happy with the main Spongebob v6 model, I trained a new one with blurring OTF options and two different downscale types. This one is much better in my opinion.

Architecture	ESRGAN
Scale	1x
Size	64nf23nb
Color Mode	RGB
License	GPL-3.0-only Private use Commercial use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2020-02-29
Dataset	Random Images, LRs were converted to Lab color, then had their a and b channel blurred with a gaussian function (random radius: 0.5 - 5.0).
Dataset size	45045
Training iterations	30000
Training epochs	3
Training batch size	15
Training HR size	128

BSChroma

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