Uploaded reference and colab

README.md

@@ -5,16 +5,19 @@ _Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) ima
 
 ![summary_SAR-CNN](./img/proposedCNN.png)
 
+## Resources
 ## Resources
 - [Paper (ArXiv)](https://arxiv.org/abs/2006.15559)
-The material is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
-All rights reserved.
+- [Remot Sensing publication](https://www.mdpi.com/2072-4292/12/16/2636)
 
 To cite the article:
+```
+Dalsasso, E.; Yang, X.; Denis, L.; Tupin, F.; Yang, W. 
+SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy. 
+Remote Sens. 2020, 12, 2636. https://doi.org/10.3390/rs12162636 
+```
 
-    @article{dalsasso2020sar,
-        title={SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy},
-        author={Emanuele Dalsasso and Xiangli Yang and Loïc Denis and Florence Tupin and Wen Yang},
-        journal={arXiv preprint arXiv:2006.15559},
-        year={2020}
-    }
+## Licence
+
+The material is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
+All rights reserved.

SAR_CNN_test.ipynb

 %% Cell type:markdown id: tags:
 
-<a href="https://colab.research.google.com/github/emanueledalsasso/SAR-CNN/blob/master/SAR_CNN_test.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
+**[Download the notebook](https://gitlab.telecom-paris.fr/ring/SAR-CNN/-/raw/master/SAR_CNN_test.ipynb?inline=false) and then import it under Google Colab**
+
+<a href="https://colab.research.google.com/" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
 %% Cell type:markdown id: tags:
 
 # SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy
 ## Emanuele Dalsasso, Xiangli Yang, Loïc Denis, Florence Tupin, Wen Yang
 
 The code is made available under the **GNU General Public License v3.0**: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school.
 All rights reserved.
 
 Please note that the training set is only composed of **Sentinel-1** SAR images, thus this testing code is specific to this data.
 
 %% Cell type:markdown id: tags:
 
 ## Resources
 - [Paper (ArXiv)](https://arxiv.org/abs/2006.15559)
+- [Remot Sensing publication](https://www.mdpi.com/2072-4292/12/16/2636)
 
 To cite the article:
-
-    @article{dalsasso2020sar,
-        title={SAR Image Despeckling by Deep Neural Networks: from a pre-trained model to an end-to-end training strategy},
-        author={Emanuele Dalsasso and Xiangli Yang and Loïc Denis and Florence Tupin and Wen Yang},
-        journal={arXiv preprint arXiv:2006.15559},
-        year={2020}
-    }
+```
+Dalsasso, E.; Yang, X.; Denis, L.; Tupin, F.; Yang, W.
+SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy.
+Remote Sens. 2020, 12, 2636. https://doi.org/10.3390/rs12162636
+```
 
 %% Cell type:markdown id: tags:
 
 ## 0. Enable GPU and save copy on Drive to enable editing
 Runtime -> Change runtime type -> Hardware accelerator: GPU
 
 File -> Save a copy in Drive
 
 %% Cell type:markdown id: tags:
 
 ## 1. Download network weights and code
 
 %% Cell type:code id: tags:
 
 ``` 
 from google_drive_downloader import GoogleDriveDownloader as gdd
 
 gdd.download_file_from_google_drive(file_id='1CgoG3f02uFzpA5PGcwKek9bitp_5T64q',
                                     dest_path='./SAR-CNN-test.zip',
                                     unzip=True)
 ```
 
 %% Cell type:markdown id: tags:
 
 ## 2. Install compatible version of tensorflow
 
 %% Cell type:code id: tags:
 
 ``` 
 !pip install tensorflow-gpu==1.13.1
 ```
 
 %% Cell type:markdown id: tags:
 
 ## A. Test on synthetic data
 
 %% Cell type:code id: tags:
 
 ``` 
 !python /content/SAR-CNN-test/main.py --test_dir=/content/test_synthetic
 ```
 
 %% Cell type:markdown id: tags:
 
 ## B. Test on real data
 Two real Sentinel-1 images can be found in the folder _/content/SAR-CNN-test/test_data/real_
 
 
 To test on custom data, upload your single channel Sentinel-1 images in a numpy array with shape [ydim, xdim].
 
 Results are stored in _/content/test_
 
 At each time a test is run, clean the _/content/test_ directory otherwise the results will be overwritten.
 
 %% Cell type:code id: tags:
 
 ``` 
 !python /content/SAR-CNN-test/main.py --real_sar=1 --test_dir=/content/test_real
 ```