SRGAN-Keras_Implementation

Implementing SRGAN - an Generative Adversarial Network model to produce high resolution photos. In this repository we have reproduced the SRGAN Paper - Which can be used on low resolution images to make them high resolution images. The link to the paper can be found here: SRGAN

Model Architechture

The model is assembled from two components Discriminator and Generator. Discriminator - Responsible to distinguish between generated photos and real photos. Generator - Generate high resolution images from low resolution images.

Generator

components list:

• 7 Convolution blocks Each block with the same number of filters
• PReLU with ( α = 0.2 ) is used as activation layer
• 2 PixelShuffler layers for upsampling - PixelShuffler is feature map upscaling
• Skip connections are used to achieve faster convergence

Discriminator

components list:

• 16 Residual blocks Each block with increasing number of filters
• LeakyReLU with ( α = 0.2 ) is used as activation layer
• 2 Dense layers

Data download link

!wget http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip

How To Use

You can run this in two ways:

• Using terminal
• Running the Notebook

If you decided the first choice follow the next steps: 0. you first need to download the data from this link

1. run this line from the terminal: python3 init.py --mode train --dir-path <path to your images folder>
2. use --help to see all the available commands: python3 init.py --help

Further work

• Create loader which doesn't hold the images in memory.
• Add a link to pre-trained model.

Results

After 350 Epochs