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

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

Purpose: "binarization" for scanned books.

A compact, lightweight version trained for general-purpose cleanup. While it performs well, RealPLSKR delivers superior results for more complex cases. 
- Strengths (Handles Well): 
✔ Color bleed 
✔ Spots & small speckles 
✔ Mould stains 
✔ Fine hair & scratches – Good at thin, faint lines. 
✔ Light pencil marks 
✔ Dividers & text preservation 
- Weaknesses (Struggles With): 
✖ Large speckles 
✖ Thick hair/clumps – May leave remnants or blurring. 
✖ Photographs – Bad lighting; noisy/dark images degrade quality.

Book Compact

Purpose: Cartoons

This is a simple retrain of SaurusX's 1x_BroadcastToStudioLite_485k model from a couple years ago. This one is trained on compact, actually has less artifacts, and is significantly faster.

__Comparisons:__ <https://slow.pics/c/oGwHyYym>
![1733071729 5752351](https://github.com/user-attachments/assets/f247b870-d49e-4c37-ad39-c4565efe164d)

BroadcastToStudio 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.

-------

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

To retain details while removing artifacts caused by dxt compression on textures

DEDXT

Attempts to remove PVRTC compression artifacts. If 1x_DEPVR is unable to remove some artifacts you should use the other version but it can't remove color artifacts properly so you should overlay color of normal version to it.

DEPVR

DEPVR Artifact

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/1xDeH264_realplksr)

# 1xDeH264_realplksr  
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, H264  
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): No  
Pretrained Model: [1xDeJPG_realplksr_otf](https://github.com/Phhofm/models/releases/tag/1xDeJPG_realplksr_otf)  
Iterations: 210'000  
Batch Size: 8  
Patch Size: 64  

Description:   
A 1x de h264 model to remove h264 compression.  

Showcase:  
[Slowpics](https://slow.pics/c/71ulMz8P?canvas-mode=fit-height)  
[Imgsli](https://imgsli.com/Mjg1NTQy)

1xDeH264_realplksr

1xDeJPG OmniSR model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_OmniSR

1xDeJPG SRFormer light model

Meant to remove jpg artifacts. First compression round on the training dataset was 40-95, second round was 80-100.

onnx conversions in this folder which contains all files: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY

Example: https://slow.pics/c/70nGEvPR

PS these are non-otf models. otf experiments have not quite done it for me (probably would need more adjusting), but they can be found in the experiments folder and their outputs in the outputs folder: https://drive.google.com/drive/folders/1RRXZCRVqlqU_iaeF-RGUM9UBWtI0HseY?usp=drive_link

DeJPG_SRFormer_light

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

1xDeJPG_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: JPEG, Restoration  
Subject: Photography  
Input Type: Images  
Release Date: 09.07.2024  

Dataset: [nomosv2](https://github.com/muslll/neosr/?tab=readme-ov-file#-datasets)  
Dataset Size: 6000  
OTF (on the fly augmentations): Yes  
Pretrained Model: This one used the 60 model as pretrain, while I used [4xNomos2_realplksr_dysample](https://github.com/Phhofm/models/releases/tag/4xNomos2_realplksr_dysample) as a pretrain for the 60 model.  
Iterations: 196'000  
Batch Size: 24  
GT Size: 64  

Description:   
A 1x dejpg model, trained with otf down to jpg 40 in both degradation processes, see the yml config file for details.  
I also release the 1xDeJPG_realplksr_otf_60 model additionally, which had been trained with less jpg compression in comparison, down to 60 in both cases, where the hope was that it could give a bit higher quality on less compressed inputs.  
The 40 model is released as default since it will be able to handle more compressed inputs in general.  
Since these are trained with otf will slightly deblur. Examples here are compressed, and then recompressed, with jpg 40.  

Showcase:  
[Slowpics](https://slow.pics/c/c0Wft9H4)  
[Imgsli](https://imgsli.com/Mjc3NzMw)

1xDeJPG_realplksr_otf

Pretrained model: 1x_DeJpeg_Fatality_01_200000_G.pth

DeJpeg Fatality PlusULTRA!

[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 

Incomplete lite model to remove ROQ compression

DeRoqBeta-lite

Debandurh_FS Ultra-lite

This is a 1x model for removing halos in animation. I was training this a while back and considered it a failed project, but I recently found it useful on a few things I was working on, so I decided to release it. I don't remember any details regarding how it was trained.

Dehalo_v1_Compact

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

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	2020-02-28
Dataset	Random Images, LRs were converted to Lab color, then had their L channel blurred with a gaussian function (rad avg: 1.5).
Dataset size	45045
Training iterations	8000
Training epochs	3
Training batch size	12
Training HR size	128

BSLuma

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