| # SAR-DDPM | |
| Code for the paper [SAR despeckling using a Denoising Diffusion Probabilistic Model](https://arxiv.org/pdf/2206.04514.pdf), acepted at IEEE Geoscience and Remote Sensing Letters | |
| ## To train the SAR-DDPM model: | |
| - Download the weights 64x64 -> 256x256 upsampler from [here](https://github.com/openai/guided-diffusion). | |
| - Create a folder ./weights and place the dowloaded weights in the folder. | |
| - Specify the paths to your training data and validation data in ./scripts/sarddpm_train.py (line 23 and line 25) | |
| - Use the following command to run the code (change the GPU number according to GPU availability): | |
| ```bash | |
| MODEL_FLAGS="--attention_resolutions 32,16,8 --class_cond True --diffusion_steps 1000 --large_size 256 --small_size 64 --learn_sigma True --noise_schedule linear --num_channels 192 --num_heads 4 --num_res_blocks 2 --resblock_updown True --use_fp16 True --use_scale_shift_norm True" | |
| export PYTHONPATH=$PYTHONPATH:$(pwd) | |
| CUDA_VISIBLE_DEVICES=0 python scripts/sarddpm_train.py $MODEL_FLAGS | |
| ``` | |
| ### Acknowledgement: | |
| This code is based on DDPM implementation in [guided-diffusion](https://github.com/openai/guided-diffusion) | |
| ### Citation: | |
| ``` | |
| @ARTICLE{perera2022sar, | |
| author={Perera, Malsha V. and Nair, Nithin Gopalakrishnan and Bandara, Wele Gedara Chaminda and Patel, Vishal M.}, | |
| journal={IEEE Geoscience and Remote Sensing Letters}, | |
| title={SAR Despeckling using a Denoising Diffusion Probabilistic Model}, | |
| year={2023}} | |
| ``` | |