SRGAN

A Tenserflow implementation of SRGAN based on CVPR 2017 paper "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network".

Usage

Requirement

python          3.9.13
tensorflow      2.11.0
numpy           1.21.5
opencv-python   4.6.0.66

Train

Put train image(DIV2K DataSet) dataset at ./train folder.

To train dataset, type the code below on your cmd.

python train.py

optional arguments:

--epochs                 epochs
--batchs                 batchs
--lr_g                   learning rate of generator
--lr_d                   learning rate of discriminator
--train_dir              directory of image to train / 학습 할 이미지 위치
--load_model             load saved model / 저장된 모델 불러오기 (1: True, 0: False)
--use_cpu                forced to use CPU only / CPU 만 이용해 학습하기 (1: True, 0: False)

Test Single Image

Put test image in ./test folder.

To test dataset, type the code below on your cmd.

python test.py

The result will be saved in ./result folder.

optional arguments:

--target_folder         directory of image to process super resolution / super resolution 처리할 이미지 위치
--save_folder           directory to save super resoultion image / super resolution 처리된 이미지 저장 위치