CARN and SRresNet

Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network [Paper] https://paperswithcode.com/paper/fast-accurate-and-lightweight-super-1

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
[Paper]https://arxiv.org/abs/1609.04802

参考资料

• Xiangtaokong/ClassSR https://github.com/Scallions/ClassSR_paddle

Paddle 复现版本

数据集

training datasets

Download the training datasets(DIV2K) and validation dataset(Set5).

Generate simple, medium, hard (class1, class2, class3) training data.

cd scripts
python generate_mod_LR_bic.py
python data_augmentation.py
python generate_mod_LR_bic.py
python extract_subimages_train.py
python divide_subimages_train.py

testing datasets

Download the testing datasets (DIV2K_valid).

Generate simple, medium, hard (class1, class2, class3) validation data.

cd scripts
python extract_subimages_test.py
python divide_subimages_test.py

训练步骤

train sr

python train.py -opt config/train/train_CARN.yml
python train.py -opt config/train/train_SRResNet.yml

测试步骤

Pre-trained weights can be found in：Pre-trained weights

python test.py -opt config/test/test_CARN.yml
python test.py -opt config/test/test_SRResNet.yml

实验结果

复现指标

	PSNR
Paddle	28.17(CARN)
Paddle	29.78(SRResNet)