a-PyTorch-Tutorial-to-Super-Resolution
Image-Super-Resolution-via-Iterative-Refinement
| a-PyTorch-Tutorial-to-Super-Resolution | Image-Super-Resolution-via-Iterative-Refinement | |
|---|---|---|
| 2 | 5 | |
| 542 | 3,364 | |
| - | - | |
| 2.7 | 0.0 | |
| about 1 year ago | 6 months ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
a-PyTorch-Tutorial-to-Super-Resolution
-
What subjects in Python do you need to know to tackle Artificial Intelligence?
Image upscaling might be a good goal to work towards first, and there are probably lots of good tutorials on it. You can follow a tutorial and Google anything from the tutorial that you don't understand. Here's one I found that looks helpful at first glance, and it includes working code: https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution
-
I have dozens of blurred images cropped from a custom YOLOv3 detection - What GAN can I use to reconstruct a single superior image?
Hi try to give a look at image super resolution task https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution . This is the first result from google. Of course the quality of the results could depend on your data (domain shift). In that case grab high quality images from internet, lower their quality and do fine tuning of the selected model
Image-Super-Resolution-via-Iterative-Refinement
- I know nothing about coding - could someone help me get something running?
-
New Super Resolution method
Github link
-
Google’s New AI Photo Upscaling Tech Is Jaw-Dropping
Here's an unofficial copy of the code: https://github.com/Janspiry/Image-Super-Resolution-via-Itera...
-
SR3: Iterative Image Enhancement
https://github.com/Janspiry/Image-Super-Resolution-via-Itera...
Imagine a game using this tech, you could render a game in a lower resolution and possible get a better looking game. But then again they aren't yet dealing with temporal data.
In the previous discussion https://news.ycombinator.com/item?id=27858893 they mentioned that it was only class conditional but it also seems to work on unconditional data.
- I have super limited coding experience but have a question about this github link.
What are some alternatives?
Real-ESRGAN - Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
Fast-SRGAN - A Fast Deep Learning Model to Upsample Low Resolution Videos to High Resolution at 30fps
Image-Super-Resolution-via-Itera
mmagic - OpenMMLab Multimodal Advanced, Generative, and Intelligent Creation Toolbox. Unlock the magic 🪄: Generative-AI (AIGC), easy-to-use APIs, awsome model zoo, diffusion models, for text-to-image generation, image/video restoration/enhancement, etc.
EGVSR - Efficient & Generic Video Super-Resolution
AnimeGAN - Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
PaddleGAN - PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.
iSeeBetter - iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks | Python3 | PyTorch | GANs | CNNs | ResNets | RNNs | Published in Springer Journal of Computational Visual Media, September 2020, Tsinghua University Press
Real-ESRGAN - PyTorch implementation of Real-ESRGAN model
pytorch-gans - PyTorch implementation of GANs (Generative Adversarial Networks). DCGAN, Pix2Pix, CycleGAN, SRGAN
Real-ESRGAN-colab - A Real-ESRGAN model trained on a custom dataset