F2SRGAN

This is an official implementation of paper "F2SRGAN: A Lightweight Approach Boosting Perceptual Quality in Single Image Super-Resolution via A Revised Fast Fourier Convolution".

Data prepare

You should organize the images layout like this:

SR_training_dataset
├── DIV2K_train_HR # Include 800 train images
├── Flickr2K
└── DIV2K_valid_HR # Include 100 test images

SR_testing_dataset
├── Set5
├── Set14
├── BSDS100
└── Urban100

Train

There are 5 mode of training:

• pre: L1 loss only.
• per: Perceptual loss only.
• gan: Perceptual loss and gan loss.
• rgan: Perceptual loss and rgan loss.
• full: The proposed loss in paper.

To replicate the paper's result, first train model with pre mode, followed by full mode.

Test

Pretrained weight for x2 and x4 upscale factor are provided in pretrain_weight. Code for load model:

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
SCALE_FACTOR = 4 #Setup scale factor
MODEL_PATH = f"./pretrain_weight/F2SRGAN_{SCALE_FACTOR}x.pt"
netG = Generator(upscale_factor = SCALE_FACTOR)
netG.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE)['model'])
model.eval()

Evaluate

• First run inference.py to produce output images.
• To measure the Perceptual Index, please refer to this Repository for more information

Citation

@ARTICLE{10077589,
  author={Nguyen, Duc Phuc and Vu, Khanh Hung and Nguyen, Duc Dung and Pham, Hoang-Anh},
  journal={IEEE Access}, 
  title={F2SRGAN: A Lightweight Approach Boosting Perceptual Quality in Single Image Super-Resolution via a Revised Fast Fourier Convolution}, 
  year={2023},
  volume={11},
  number={},
  pages={29062-29073},
  doi={10.1109/ACCESS.2023.3260159}}

References

• SwiftSRGAN
• LaMa