upscaling old anime. help to denoise and find lines and dehalo

BIGOLDIES

Category: Digital Animation


This was trained on the dataset that OptimusPrimal used for his DigitalFilm models. This model cleans up the image removing some noise while upscaling. This model is very fast, running around 200x the speed of a standard ESRGAN model on my system. This is primarily a proof of concept for how fast Real-ESRGAN models can get while still producing nice results.

DigitalFlim SuperUltraCompact

A 2x Compact anime upscale model that handles AVC (h264) degradation. Applied h264 crf 20-28 degradation together with bicubic, bilinear, box and lanczos downsampling on musl's HFA2k dataset with Kim's dataset destroyer.

2xHFA2kAVCCompact

A 2x EDSR M anime upscale model that handles AVC (h264) degradation. Applied h264 crf 20-28 degradation and bicubic, bilinear, box and lanczos downsampling on musl's HFA2k dataset with Kim's dataset destroyer.

2xHFA2kAVCEDSR_M

The second released community trained model on the OmniSR network. Trained with multiscale discriminator to fix decreased output brightness occuring with OmniSR. 2x anime upscale that handles AVC (h264) compression since h264 crf 20-28 degradation together with bicubic, bilinear, box and lanczos downsampling has been applied on musl's HFA2k dataset with Kim's dataset destroyer.

2xHFA2kAVCOmniSR

2x SRFormer_light anime upscale model that handles AVC (h264) compression since h264 crf 20-28 degradation together with bicubic, bilinear, box and lanczos downsampling has been applied on musl's HFA2k dataset with Kim's dataset destroyer for training.


If you want to run this model with chaiNNer (or another application) you need to use the onnx files with an onnx upscale node. All onnx conversions can be found in the [onnx folder on my repo](https://github.com/Phhofm/models/tree/main/2xHFA2kAVCSRFormer_light/onnx).

Example 1: https://imgsli.com/MTkxMTQz

Example 2: https://imgsli.com/MTkxMTQ0

2xHFA2kAVCSRFormer_light

I trained Skr's great LD-Anime model on compact architecture. It upscales while fixing numerous video problems, including: noise/grain, compression artifacts, rainbows, dot crawl, halos and color bleed. This compact version may look slightly worse than Skr's original model, but runs significantly faster and also retains the correct colors better than the original model did.

LD-Anime Compact

Purpose: Denoise/Dehalo

Denoise, dehalo, derainbow old anime

LD-Anime_Skr v1.0

SPAN model for live action film and digital video. The main goal is to fix/reduce common video quality problems while maintaining fidelity. I tried the existing video-focused models and they all denoise or cause colour shifts so I decided to train my own.

The model is trained with compression (JPEG, MPEG-4 ASP, H264, VP9, H265), chroma subsampling, blurriness from multiple scaling, uneven horizontal and vertical resolution, oversharpening halos, bad deinterlacing jaggies, and onscreen text. It is not trained to remove noise at all so it preserves details in the source well. To prevent colour/brightness shifts, I used consistency loss in neosr. I had to modify consistency loss to use a stronger blur so it doesn't interfere with the halo removal.

Limitations:
1. The model has limited ability to see details through heavy grain, but light to moderate grain is fine.
2. The model still does not handle bad deinterlacing perfectly, especially if the source is vertically resized. Fixing bad deinterlacing is not the main goal so it is what it is. Sources that are line-doubled throughout should be descaled back to half height first for best results.
3. The model sometimes oversharpens a little. This is probably because the training data has some oversharpened images.
4. This model generally cannot handle VHS degradation.

More comparisons: https://slow.pics/c/DtDN7gaq

The training config and image degradation scripts used to create training data can be found in https://github.com/jcj83429/upscaling/tree/9332e7d5b07747ff347e5abdc43f8144364de9f7/2xLiveActionV1_SPAN

2xLiveActionV1_SPAN

Purpose: Compression Removal, Noise Reduction, Line Correction, MPEG2 / LD Artifact Removal

This is my first model release. The model was inspired from a personal project which I have been pursuing for some time now, which this model aims to solve. This model will upscale low resolution hand drawn animation from 1970s to 2000. The colors are retained with effective noise control. Details and Textures are maintained to a good degree considering animations. Color Spills are also corrected depending on the colors. Shades of white and yellows have been difficult. It also makes the lines slightly sharper and thinner. This could be a plus depending on your source. The model is also temporally stable across my tests with little observable issues. 

**Showcase:** 
Images - https://imgsli.com/MjYwNzY1/12/13
Video Sample -  https://t.ly/Jp7-w vs Upscale - https://t.ly/PdsKs

2x Pooh V4

Purpose: Upscale text in very low quality to normal quality.

The upscale model is specifically designed to enhance lower-quality text images, improving their clarity and readability by upscaling them by 2x. It excels at processing moderately sized text, effectively transforming it into high-quality, legible scans. However, the model may encounter challenges when dealing with very small text, as its performance is optimized for text of a certain minimum size. For best results, input images should contain text that is not excessively small.

Text2HD v.1

Category: VHS Tapes
Purpose: VHS

VHS captures of Film material, but may work on VHS recorded native SD-TV material as well. Also useable for cleaned up source material

VHS upscale and denoise Film

Purpose: VHS Restoration

An advanced VHS recording model designed to enhance video quality by reducing artifacts such as haloing, ghosting, and noise patterns. Optimized primarily for PAL resolution (NTSC might work good as well).

VHS2HD

Purpose: 

Emulating Waifu2x at Noise Level 3 NOTE: You can't use this with regular esrgan forks or the bot, it has to be run through basicsr

Waifaux NL3 SRResNet

Purpose: 

Trained this model to see how it would work trying to essentially get the same results as Waifu2x but with ESRGAN

Waifaux NL3 SuperLite

Upscaling model for 1990s cartoon series "Where on Earth is Carmen Sandiego?" May also be useful for other shows of that era.

Note: Before using this model, you will need to either:
* Deinterlace the episodes (I recommend QTGMC on Slower) to 60 fps (I recommend 60 fps because that's the rate of the live-action footage and some other components).
* Repair the existing deinterlacing via one of the related models for this show.
Which method will depend on the episode and release. See the related deinterlacing models for details.

This show is...complicated. Unlike, say, the Super Mario Bros. Super Show, where live action and animated segments are generally separated into large chunks, Where on Earth is Carmen Sandiego repeatedly switches between live-action, traditional animation, 3D CGI, 2D computer graphics, stock photography, and other sources, sometimes combining them in the same frame. As such, rather than create multiple models for each type, I've created a single model for all footage types.

Known Weaknesses:
* The model performs less well on the live-action (Player) and 3D CGI (C5 Corridor) segments due to a scarcity of usable data in those scenes.
* I don't own the 2025 DVD release, so I can't comment on how well this model works on that footage.

Where On Earth-Upscale-Large

Where On Earth-Upscale-Small

DeIndeo

Description: 4x realistic upscaler that may also work for general purpose usage. It was trained with OTF random degradation with a very low to very high range of degradations, including blur, noise, and compression. Trained with the same Frankendata dataset that I used for the pretrain model.

Frankendata_FullDegradation_SRFormer

Purpose: Clean Real-World Images

Upscaling of realistic images/photos with noise and compression artifacts

NMKD Superscale

i made this model to upscale old anime and denoise.

OLDIES_FINAL

UniScale_Restore has strong compression removal that helps with restoring heavily compressed or noisy images. It is intended to compete with BSRGAN. Trained with BSRGAN_Resize and Combo_Noise in traiNNer.

UniScale Restore

Version of UniScale trained with camera noise injection (NR = Noise Removal). This model removes noise from images while upscaling.

UniScaleNR Balanced

UniScaleNR Strong

Purpose: Game textures/Art

This model upscales artwork for the game Volfoss (2001). The model peaked in quality very quickly. The NR model removes most noise, but has the downside of removing transparent portions. Use the main model in most cases.

Architecture	ESRGAN
Scale	2x
Size	64nf23nb
Color Mode	RGB
License	CC-BY-NC-SA-4.0 Private use Distribution Modifications Credit required Same License State Changes No Liability & Warranty Disclaimer
Date	2021-03-28
Dataset	For LR, two decade old VHS recording of a movie from TV, captured with enabled TBC on the VCR. For HR, Blu-ray rip of that movie.
Training iterations	477000
Training epochs	2733
Training batch size	7
Training HR size	128

VHS upscale and denoise Film

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