Super Resolution Generative Adversial Network
We are
Ramon Vallés - 205419
Eduard Masip - 207322
Ferran Enguix - 195659
This repository includes:
The project code in .ipynb format.
The project code in .py format.
Two scripts in .py format to create new .mat files and slice images, both commented.
We cannot update the .mat file to train the model because the file is so big!
The .mat file with 100 of images from DIV2K Dataset (TEST).
The .mat file with few images of pixel-art to test the results in other kind of images (TEST).
Drive Link to obtain the MAT files: https://drive.google.com/drive/folders/1eiYDPu9iPxuuSZEdfP_Vap4kzNgVBPsU?usp=sharing
The idea of this project is obtain images with more resolution applying SRGAN.
We use the architecture of this paper, with some changes: https://arxiv.org/abs/1609.04802.
We use this dataset: https://data.vision.ee.ethz.ch/cvl/DIV2K/.
This SRGAN is trained and programed to increment by 2 the resolution, we use images of 64x64 to obtain 128x128 images.
But it can be used to obtain 128x128 to 256x256, we doesn't test the results.
To upscale the resolution more than 2 is necessary change a little bit the architecture!!