Data Science by ODS.ai 🦜
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First Telegram Data Science channel. Covering all technical and popular staff about anything related to Data Science: AI, Big Data, Machine Learning, Statistics, general Math and the applications of former. To reach editors contact: @haarrp
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​​Super-resolution GANs for improving the texture resolution of old games.

It is what it is. #GAN to enhance textures in old games making them look better.

ArXiV: https://arxiv.org/abs/1809.00219
Link: https://www.gamespot.com/forums/pc-mac-linux-society-1000004/esrgan-is-pretty-damn-amazing-trying-max-payne-wit-33449670/

#gaming #superresolution
​​Neural net to enhance old or low-quality video to HD (TS -> HD).

It is so surprising that noone had yet released a model for that. People have lots of old video recordings, which will definately benefit from quality enhancement. And we all have to hope movie pirates won’t use it to enhance stolen copies.

Link: https://news.developer.nvidia.com/researchers-at-videogorillas-use-ai-to-remaster-archived-content-to-4k-resolution-and-above/
More demos: https://videogorillas.com/bigfoot/

#SuperResolution #CV #DL
​​Real-World Super-Resolution of Face-Images from Surveillance Cameras

Most SR methods are trained on LR (low resolution) data, which is downsampled from HR (high resolution) data using bicubic interpolation, but real-life LR images are usually different, so models work worse on them. In this paper, the authors suggest using blur kernels, noise, and JPEG compression artifacts to generate LR images similar to the original ones.
Another suggested improvement is using ESRGAN and replacing VGG-loss with LPIPS-loss, as well as adding PatchGAN.
In addition, the authors show that NIMA metric better correlates with human perception (mean opinion rank) than traditional Image Quality Assessment methods.

Paper: https://arxiv.org/abs/2102.03113

#deeplearning #superresolution #gan #facesuperresolution
MMPX Style-Preserving Pixel Art Magnification

Work on #pixel graphics resolution upscale. Hopefully we will get all the classic games auto-remastered someday.

Publication: http://www.jcgt.org/published/0010/02/04/
Article: http://www.jcgt.org/published/0010/02/04/paper.pdf

#CV #superresolution #upscale
​​SwinIR: Image Restoration Using Swin Transformer

Image restoration is a long-standing low-level vision problem that aims to restore high-quality images from low-quality images (e.g., downscaled, noisy, and compressed images). While state-of-the-art image restoration methods are based on convolutional neural networks, few attempts have been made with Transformers, which show impressive performance on high-level vision tasks.

The authors use a model SwinIR based on the Swin Transformers. Experimental results demonstrate that SwinIR outperforms state-of-the-art methods on different tasks (image super-resolution, image denoising, and JPEG compression artifact reduction) by up to 0.14~0.45dB, while the total number of parameters can be reduced by up to 67%.

Paper: https://arxiv.org/abs/2108.10257
Code: https://github.com/JingyunLiang/SwinIR

A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-swinir

#deeplearning #cv #transformer #superresolution #imagerestoration