Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Overview

An unofficial implementation of SRGAN described in the paper using PyTorch.

• Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Published in CVPR 2017

Requirement

• Python 3.6.5
• PyTorch 1.1.0
• Pillow 5.1.0
• numpy 1.14.5
• scikit-image 0.15.0

Datasets

• DIV2K

Pre-trained model

• SRResNet

• SRGAN

Train & Test

Train

python main.py --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir

Test

python main.py --mode test --LR_path ./LR_imgs_dir --GT_path ./GT_imgs_dir --generator_path ./model/SRGAN.pt

Inference your own images

python main.py --mode test_only --LR_path ./LR_imgs_dir --generator_path ./model/SRGAN.pt

Experimental Results

Experimental results on benchmarks.

Quantitative Results

Method	Set5	Set14	B100
Bicubic	28.43	25.99	25.94
SRResNet(paper)	32.05	28.49	27.58
SRResNet(my model)	31.96	28.48	27.49
SRGAN(paper)	29.40	26.02	25.16
SRGAN(my model)	29.93	26.95	26.10

Qualitative Results

Bicubic	SRResNet	SRGAN

Comments

If you have any questions or comments on my codes, please email to me. son1113@snu.ac.kr