This is the pytorch implementation of the paper (accpted by IEEE TCSVT 2023).
download the Aster model from https://github.com/ayumiymk/aster.pytorch, Moran model from https://github.com/Canjie-Luo/MORAN_v2, CRNN model from https://github.com/meijieru/crnn.pytorch.
Change TRAIN.VAL.rec_pretrained in src/configs/super_resolution.yaml to your Aster model path, change TRAIN.VAL.moran_pretrained to your MORAN model path and
change TRAIN.VAL.crnn_pretrained to your CRNN model path.
Change TRAIN.train_data_dir0 to your train data path.
Change TRAIN.VAL.val_data_dir0 to your val data path.
- train with textzoom
cd ./src/
python3 main.py --batch_size=30 --STN --mask --gradient --vis_dir='demo1'
- test with textzoom
python3 main.py --batch_size=1024 --test --test_data_dir='your-test-lmdb-dataset' --resume='your-model.pth' --STN --mask --gradient --vis_dir='vis'
- demo with images
python3 main.py --demo --demo_dir='./images/' --resume='your-model.pth' --STN --mask
If you have any question, please contact us without hesitation.
If you find TEAN useful in your research, please consider citing.
@ARTICLE{10102515, author={Shu, Rui and Zhao, Cairong and Feng, Shuyang and Zhu, Liang and Miao, Duoqian}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, title={Text-Enhanced Scene Image Super-Resolution via Stroke Mask and Orthogonal Attention}, year={2023}, volume={33}, number={11}, pages={6317-6330}, doi={10.1109/TCSVT.2023.3267133}}