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# Super Resolution | ||
# Photo-Realistic Single Image Super-Resolution GAN | ||
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![TensorFlow](https://img.shields.io/badge/TensorFlow-%23FF6F00.svg?style=for-the-badge&logo=TensorFlow&logoColor=white) ![Keras](https://img.shields.io/badge/Keras-%23D00000.svg?style=for-the-badge&logo=Keras&logoColor=white) ![nVIDIA](https://img.shields.io/badge/nVIDIA-%2376B900.svg?style=for-the-badge&logo=nVIDIA&logoColor=white) | ||
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) | ||
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> [!NOTE]\ | ||
> I adapted the code from [this awesome PyTorch version](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution). Please check it out as well. | ||
> [!IMPORTANT]\ | ||
> I am using python `3.11` with tensorflow `2.12` on WSL2. | ||
## Steps | ||
1. `pip install -r requirements.txt` | ||
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## Overfitting on one image | ||
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![bird_resolved](https://github.com/AndreiMoraru123/Super-Resolution/assets/81184255/1429d7c7-96be-4737-be21-253ac5a09ed2) | ||
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``` | ||
Evaluation results: | ||
{ 'PSNR (SRGAN)': 29.44381, | ||
'PSNR (SRResNet)': 29.380556, | ||
'SSIM (SRGAN)': 0.87486875, | ||
'SSIM (SRResNet)': 0.87476} | ||
``` |