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

	@article{2019ChangInfrared,
	  title={Infrared Aerothermal Nonuniform Correction via Deep Multiscale Residual Network},
	  author={Yi Chang, Luxin Yan, Li Liu, Houzhang Fang, and Sheng Zhong},
      journal={IEEE Geosci. Remote Sens. Lett.},
	  year={2019}
	}

### Main Contents

main_start_ms.m:  training demo.

get_test_ms.m:  testing demo

get_train_initial_multiscale.m: setting the CNN structure 

You should revise the file path with our own data. 

We have provided an example for testing in \Testdata folder. And also we have released several pre-trained models for various noise cases. 

To run the training and testing demos, you should first download [MatConvNet](http://www.vlfeat.org/matconvnet/). The detailed installing procedure for Windows OS can be found (https://www.youtube.com/watch?v=DWNyp1xZ-ks).

After you have installed the MatconvNet, you should place the vllab_dag_loss.m and Tanh.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 from the Place2 dataset. 

The generating function is GenerateData.m. You can also generate the training dataset on your own.

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

