
### Citation
If you find TSWEU useful in your research, please consider citing:

	@article{2020ChangToward,
	  title={Toward Universal Stripe Removal via Wavelet-Based Deep Convolutional Neural Network},
	  author={Yi Chang, Meiya Chen, Luxin Yan, Xi-le Zhao, Yi Li, and Sheng Zhong},
      journal={IEEE Trans. Geosci. Remote Sens.},
	  volume={58},
      number={4},
      pages={2880-2897},
	  year={2020}
	}

### Main Contents

main_start_ms.m:  training demo.

get_test_ms.m:  testing demo

get_train_initial.m: setting the CNN structure 

We have released several pre-trained models for different non-periodical stripe noise levels. 

To run the training and testing demos, you should first download [MatConvNet](http://www.vlfeat.org/matconvnet/).

After you have installed the MatconvNet, you should place the vl_nndwt2 and vl_nniwt2 p-code into the matconvnet-1.0-beta25\matlab folder, IWT2HD.m and DWT2HD.m into the matconvnet-1.0-beta25\matlab\+dagnn folder, and backward.m and forward.m into matconvnet-1.0-beta25\matlab\+dagnn\@DagNN folder.

### Training dataset generating

We generate the training dataset with 2W images from the Place2 dataset. The generating rules follow the similar way as that of DnCNN. You can generate the training dataset on your own.

If you have any questions about the codes, please contact yichang@hust.edu.cn for further information.

