SRGAN is a neural network architecure firstly introduced in the following paper for upscaling images.
This repository contains the source code for the implementation of the SRGAN discussed in the following paper link. The model was trained in the following way : firstly, the generator is trained with a non GAN approach by minimizing the MSE loss function between the upscaled image and reconstructed image for roughy 40.000 iteration of 32 batches each on a Google Colab notebook (free-tier); next, both discriminator and generator are trained with a GAN approach for approximately 20.000 iterations on Google Colab (Pro version).
The dataset (available at the following kaggle link) on which the model was trained is organized in the following way : a folder containing various subfolders, each associated with an id, containing the original image and the downsampled image. The downsampled image was obtained by executing the scripting downsampling.py, which applies a 4x downsampling factor to a specific image. The organization of the folders in different subfolders was realized through a bash script.
The model's weights are available here.