SAR2SAR

This projet is an implementation in pytorch of SAR2SAR, a denoised deep neural network for SAR images denoising. The network was trained on TerraSAR-X images

To run this code you must have :

• Pytorch
• numpy
• opencv
• matplotlib

Predictions

To make prediction with this network you have to :

• put .npy images in 'data/real'
• in a cmd type 'python predict.py'
• results will be stored in 'data/results/real'

If you want to add artificial speckle on your images you have to set add_speck to True line 19 of predict.py

Training

To train the network you have to :

• put phase A .npy train images in data/train_A
• put phase A .npy eval images in data/eval
• put phase B & C train images in data/train_BC. Each image pile must be in a different folder, if you have only one pile create a single folder
• put phase B & C eval images in data/eval_real
• in a cmd type 'python train_ABC.py'
• network weights will be stored in 'pipeline/out/unsupervised'
• in data/sample you will have a denoised version of eval images for each epoch