Improve README lisibility (add SAR2SAR section)

README.md

@@ -1,51 +1,51 @@
 # deepdespeckling 
-## Synthetic Aperture Radar (SAR) images despeckling with Pytorch
 
-Speckle fluctuations seriously limit the interpretability of synthetic aperture radar (SAR) images. This package provides despeckling methods that are leveraging deep learning to highly improve the quality and interpretability of SAR images. Both Stripmap and Spotlight operations are handled by this package. 
-
-The package contains both inference and training parts, wether you wish to despeckle a set of SAR images or use our model to build or improve your own.
+Speckle noise seriously limits the interpretability of synthetic aperture radar (SAR) images. This package provides <strong>deep learning based despeckling methods</strong> to denoise SAR images. 
+
+<strong>deepdespeckling</strong> gives access to two differents methods: 
+
+* [MERLIN](https://arxiv.org/pdf/2110.13148.pdf) (inference and training)
+* [SAR2SAR](https://arxiv.org/pdf/2006.15037.pdf) (inference only)
 
-To get a test function using Tensorflow's framework : https://gitlab.telecom-paris.fr/ring/MERLIN/-/blob/master/README.md
 
 [![PyPI version](https://badge.fury.io/py/deepdespeckling.svg)](https://badge.fury.io/py/deepdespeckling)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 ## Installation
 
-Install deepdespeckling by running in the command prompt :
+Before installing deepdespeckling, make sure to install gdal dependancies, it can be done using conda with the following command : 
+```
+conda install -c conda-forge gdal
+```
+
+Then, deepdespeckling can be installed simply using pip :
 
 ```python
 pip install deepdespeckling
 ```
 
-## Authors
 
-* [Emanuele Dalsasso](https://emanueledalsasso.github.io/) (Researcher at ECEO, EPFL)
-* [Hadrien Mariaccia](https://www.linkedin.com/in/hadrien-mar/) (Hi! PARIS Machine Learning Research Engineer)
-
-## Former contributors 
-
-* [Youcef Kemiche](https://www.linkedin.com/in/youcef-kemiche-3095b9174/) (Former Hi! PARIS Machine Learning Research Engineer)
-* [Pierre Blanchard](https://www.linkedin.com/in/pierre-blanchard-28245462/) (Former Hi! PARIS Engineer)
+## Inference with MERLIN
 
+To despeckle SAR images using MERLIN, images need to be in `.cos` or `.npy` format.
 
-## Examples
+In order to get the right model, the `model_name` has to be specified in the `get_denoiser()` and the `get_model_weights_path()` functions calls.
 
-Deepdespeckling offers 2 differents methods to despeckle SAR images: 
+This `model_name` can either be :
+- `"spotlight"` for SAR images retrieved with spotlight mode 
+- `"stripmap"` for SAR images retrieved with stripmap mode
 
-* [MERLIN] (https://arxiv.org/pdf/2110.13148.pdf)
-* [SAR2SAR] (https://arxiv.org/pdf/2006.15037.pdf)
 
-### Despeckle one image
+### Despeckle one image with MERLIN
 
 ```python
 from deepdespeckling.utils.load_cosar import cos2mat
 from deepdespeckling.utils.constants import PATCH_SIZE, STRIDE_SIZE
 from deepdespeckling.despeckling import get_model_weights_path, get_denoiser
 
-# Path to one image (cos, tiff or npy file, depending on the model you want to use), can also be a folder of several images
+# Path to one image (cos or npy file), can also be a folder of several images
 image_path="path/to/cosar/image"
-# Model name, can be "spotlight", "stripmap" or "sar2sar"
+# Model name, can be "spotlight" or "stripmap"
 model_name = "spotlight"
 
 image = cos2mat(image_path).astype(np.float32)
@@ -57,7 +57,7 @@ denoised_image = denoiser.denoise_image(
                 image, model_weights_path, patch_size=PATCH_SIZE, stride_size=STRIDE_SIZE)
 ```
 
-### Despeckle a set of images
+### Despeckle a set of images using MERLIN
 
 For each of this method, you can choose between 3 different functions to despeckle a set of SAR images contained in a folder : 
 
@@ -125,8 +125,46 @@ Noisy cropped image                     |           Denoised cropped image
 :-----------------------------------------------------------:|:------------------------------------------:
  <img src="img/crop/noisy_test_image_data.png" width="100%"> | <img src="img/crop/denoised_test_image_data.png" width="1000%">
 
+## Inference with SAR2SAR
+
+To despeckle SAR images using SAR2SAR, images need to be in `.tiff` or `.npy` format.
+
+In order to get the right model, the `model_name` has to be specified in the `get_denoiser()` and the `get_model_weights_path()` functions calls.
+
+The `model_name` has therefore to be set to `"sar2sar"`. Then, <strong>the despeckling functions work the same as with MERLIN.</strong>
+
+
+### Despeckle one image with SAR2SAR
+
+```python
+from deepdespeckling.utils.load_cosar import cos2mat
+from deepdespeckling.utils.constants import PATCH_SIZE, STRIDE_SIZE
+from deepdespeckling.despeckling import get_model_weights_path, get_denoiser
+
+# Path to one image (tiff or npy file), can also be a folder of several images
+image_path="path/to/cosar/image"
+# Model name, can be "spotlight" or "stripmap"
+model_name = "sar2sar"
+
+# Works exactly the same as with MERLIN
+image = cos2mat(image_path).astype(np.float32)
+# Get the right model
+denoiser = get_denoiser(model_name=model_name)
+model_weights_path = get_model_weights_path(model_name=model_name)
+
+denoised_image = denoiser.denoise_image(
+                image, model_weights_path, patch_size=PATCH_SIZE, stride_size=STRIDE_SIZE)
+```
+
+- Example of result with SAR2SAR :
+
+
+Noisy image             |  Denoised image
+:----------------------:|:-------------------------:
+![](img/entire/sar2sar_noisy.png)  |  ![](img/entire/sar2sar_denoised.png)
+
 
-### Train a new model
+## Training MERLIN
 
 1) I want to train my own model from scratch:
 ```python
@@ -172,13 +210,16 @@ model=create_model(Model, batch_size=12, val_batch_size=1, device=torch.device("
 fit_model(model, lr, nb_epoch, training_set_directory, validation_set_directory, sample_directory, save_directory, seed=2)
 ```
 
-# License
+## Authors
 
-* Free software: MIT
+* [Emanuele Dalsasso](https://emanueledalsasso.github.io/) (Researcher at ECEO, EPFL)
+* [Hadrien Mariaccia](https://www.linkedin.com/in/hadrien-mar/) (Hi! PARIS Research Machine Learning Engineer)
 
-# FAQ
+## Former contributors 
+
+* [Youcef Kemiche](https://www.linkedin.com/in/youcef-kemiche-3095b9174/) (Former Hi! PARIS Research Machine Learning Engineer)
+* [Pierre Blanchard](https://www.linkedin.com/in/pierre-blanchard-28245462/) (Former Hi! PARIS Engineer)
 
-* Please contact us at [[email protected]]([email protected])
 
 # References