Implementation codes and datasets for the paper "Learning from Clutter: An Unsupervised Learning-Based Clutter Removal Scheme for GPR B-Scans" at http://dx.doi.org/10.1109/JSTARS.2024.3486535.
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The simulation and real measurement data have been uploaded at https://drive.google.com/drive/folders/1gR2lsL1AtzI4L6VbC28ix7POcxXj5pkB?usp=sharing.
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For the diffusion model used for data augmentation in Stage 1, we refer to the usage at https://github.com/lucidrains/denoising-diffusion-pytorch. Get into our folder "Stage1_Diffusion" and run "python train.py".
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For the CUT model adopted for clutter estimation in Stage 2, we used the codes at https://github.com/taesungp/contrastive-unpaired-translation. Get into our folder "Stage2_CUT" and run
commands for training:
python train.py \
--dataroot ./dataset_name \
--name dataset_name \
--model cut \
--preprocess none \
--n_epochs 200 \
--n_epochs_decay 200 \
--load_size 128 \
--crop_size 128
commands for testing:
python test.py \
--dataroot ./dataset_name \
--name dataset_name \
--CUT_mode CUT \
--num_test 10 \
--phase test
- If any issues, pls contact [email protected].