This repository is for SADN introduced in the following paper
ZhiWei Liu,XiaoFeng Mao,Ji Huang,MengHan Gan andYueYuan Zhang, "Stratified Attention Dense Network for Image Super-Resolution".
- python >= 3.7
- tensorflow == 1.14
- numpy == 1.15.4
Stratified Attention Dense Network was proposed to reconstruct high quality HR image. Stratified Attention Dense Network
1.Download DIV2K training data(800 training + 100 validation images) from DIV2K dataset
Example command is in the file 'demo.txt'.
python main.py --train_GT_path F:/ARDN/DataSet/DIV2K_train_HR --train_LR_path F:/ARDN/DataSet/DIV2K_train_LR_bicubic/X2/ --test_GT_path F:/ARDN/DataSet/benchmark/Set5/HR/ --test_LR_path F:/ARDN/DataSet/benchmark/Set5/LR_bicubic/X2/ --test_with_train True --scale 2 --log_freq 1 --model_save_freq 10000 --max_step 10000 --n_ARDG 12 --n_ARDB 12
1.Download benchmark datasets from SNU_CVLab
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(optional) Download pretrained models for our paper.
The Trained x2 models can be downloaded from Google Drive
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Run the following scripts. Example command is in the file 'demo.txt'.
python main.py --mode test --pre_trained_model ./model/ARDN_X2_64_8_8-1000000 --test_GT_path F:/ARDN/DataSet/benchmark/Urban100/HR/ --test_LR_path F:/ARDN/DataSet/benchmark/Urban100/LR_bicubic/X2/ --scale 2 --save_test_result False --test_set Urban100+ --self_ensemble True --chop_forward True