This repo contains implementations of papers
Two methods are being used to perform super resolution
- EDSR + Pixel Shuffling + Perceptual Loss
- Pre-trained GAN (traning Generator and critic seperately and then training both)
- Pytorch>=0.3
- Fastai
$ python super.py
- ImageNet 10% sample
- Celeb A
Requires Fastai==0.7 (super_res_edsr.py)
In this method, We're making use of Pixel shuffle along with EDSR (Removal of BN in skip connection)
Original image
Prediction
Input image
Prediction
.
- FastAI>=1.45
- Pytorch >= 1.0
Use crappification from (utils.py)
We use a Unet Generator which has Resnet34/50 (Pretrained) as it's head (modifiable) Generator is trained with low quality images with ground truth being images from original dataset.
When pretraining generator only, here's the output:
Input----------------------------------------------------Prediction------------------------------------------Target
Critic is trained on generator images and original images
Check the path variables carefully
$ python3 super_res_gan.py