The official implementation of Low-dose CT image super-resolution with noise suppression based on prior degradation estimator and self-guidance mechanism.
This repository is modified from BasicSR. Thanks for the open source code of BasicSR.
conda create -n new_env python=3.9.7 -y
conda activate new_env
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt
pip install -e .
More details could be found in the installation ducoment of BasicSR.
You should modify the path in configuration files in "opations/train/*.yml" or "opations/test/*.yml".
python basicsr/train.py -opt options/train/train_pde.yml
python basicsr/train.py -opt options/train/train_self_guidance_sr_x2_aapm.yml
or
python basicsr/train.py -opt options/train/train_self_guidance_sr_x4_aapm.yml
python basicsr/train.py -opt options/test/test_self_guidance_sr_x2_aapm.yml
or
python basicsr/train.py -opt options/test/test_self_guidance_sr_x4_aapm.yml