URSCT-SESR: Reinforced Swin-Convs Transformer for Simultaneous Underwater Sensing Scene Image Enhancement and Super-resolution
Tingdi Ren, Haiyong Xu, Gangyi Jiang, Mei Yu, Xuan Zhang, Biao Wang, and Ting Luo.
This repository is the official PyTorch implementation of URSCT-SESR: Reinforced Swin-Convs Transformer for Simultaneous Underwater Sensing Scene Image Enhancement and Super-resolution
We have put demo data in folder "./dataset", hence you can run any file "*_train.py" in folder "./scripts".
If you want to use the pre-trained model for realistic images or testing, please read the following content about data settings. After that, run any file "*_eval.py" in folder "./scripts".
If you have downloaded the pre-trained model and intend to continue training/fine-tuning, please note:
- Since the code updating, the pre-trained weight data (a dict in python) uploaded before does not include any parameter about the optimizer. Hence, please reasonably set up the optimizer (e.g., a tiny learning rate).
- The default model loaded when resuming is "*_bestSSIM.pth" (at line 84/85 in the training code), please check the model file name.
1. Put your dataset into your folder storing data (for example "./dataset/demo_data_Enh") as follows:
URSCT-SESR
├─ other files and folders
├─ dataset
│ ├─ demo_data_Enh
│ │ ├─ train_data
│ │ │ ├─ input
│ │ │ │ ├─ fig1.png
│ │ │ │ ├─ ...
│ │ │ ├─ target
│ │ │ │ ├─ fig1.png
│ │ │ │ ├─ ...
│ │ ├─ val_data
│ │ │ │ ├─ ...
│ │ ├─ test_data
│ │ │ │ ├─ ...
If you want to train with the default setting, *_DIR of TRAINING and TEST is the main option you need to edit.
(1) Enh&SR_opt.yaml for Simultaneous Underwater Sensing Scene Image Enhancement and Super-resolution
(2) Enh_opt.yaml for Underwater Sensing Scene Image Enhancement only
1. As reported above, put your dataset for testing and model we provided into the folders as follows:
URSCT-SESR
├─ other files and folders
├─ exps
│ ├─ quickstart_Enh (same as configurated above)
│ │ ├─ models
│ │ ├─ model_bestSSIM.pth (downloaded model)
├─ dataset
│ ├─ demo_data_Enh
│ │ ├─ train_data
│ │ ├─ val_data
│ │ ├─ test_data
│ │ │ ├─ input
│ │ │ │ ├─ fig1.png
│ │ │ │ ├─ ...
│ │ │ ├─ target
│ │ │ │ ├─ fig1.png
│ │ │ │ ├─ ...
(1) GoogleDrive
(2) BaiduDisk (Password: SESR)
(1) LSUI (UIE): Data Paper Homepage
(2) UIEB (UIE): Data Paper Homepage
(3) SQUID (UIE): Data Paper Homepage
(4) UFO (SESR): Data Paper Homepage
(5) USR (SR): Data Paper Homepage
@article{ren2022reinforced,
title={Reinforced Swin-convs Transformer for Simultaneous Underwater Sensing Scene Image Enhancement and Super-resolution},
author={Ren, Tingdi and Xu, Haiyong and Jiang, Gangyi and Yu, Mei and Zhang, Xuan and Wang, Biao and Luo, Ting},
journal={IEEE Transactions on Geoscience and Remote Sensing},
year={2022},
publisher={IEEE}
}