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better than the supervised learning methods.\nIt has potential in various applications.\nDetails can be found on the [project page](http://chuangniu.info/projects/noise2im/) and in the [paper](https://arxiv.org/abs/2011.03384).\n\n## News\nJan 6, 2022, noise2sim 0.1.2 released\n\nDec 24, 2021, noise2sim 0.1.0 released\n\n\n## Installation\n[Pytorch](https://pytorch.org/) is required, for example,\n```shell script\nconda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch  # install pytorch\n```\nThen, Noise2Sim can be easily installed through pip,\n```shell script\npip install noise2sim\n```\nOr Noise2Sim can be installed from source,\n```shell\ngit clone https://github.com/niuchuangnn/noise2sim.git\ncd noise2sim\npip install -e .\n```\n\n## Train denoising model with Noise2Sim\n\n### Low-dose CT Images\nThe low-dose CT dataset can be obtained at [Low Dose CT Grand Challenge](https://www.aapm.org/grandchallenge/lowdosect/).\nThe preprocessed FDA data can be obtained [here](https://drive.google.com/drive/folders/1ggt6FxBTwmMa8paDgOGQOutu4bNV26Si?usp=sharing).\n\nArrange the Mayo data like:\n\n    ├── datasets   \n        ├── Mayo                   \n            ├── L067                    \n            ├── L096\n            ...\n\nList training and testing files:\n```shell\nfrom noise2sim.tools import prepare_ldct\npython prepare_ldct.py --patient-folder L067 L096 L109 L143 L192 L286 L291 L310 --output-file ./datasets/Mayo/mayo_train.txt\npython prepare_ldct.py --patient-folder L506 L333 --output-file ./datasets/Mayo/mayo_test.txt\n```\nRun on 4 GPUs:\n```shell\npython ./scripts/train.py --config-file ./configs/ldct_mayo_unet2.py # for Mayo dataset\n```\n```shell\npython ./scripts/train.py --config-file ./configs/ldct_fda_unet2.py # for FDA dataset\n```\n\n### Photon-counting CT Images\nThe photon-counting datasets can be obtained [here](https://drive.google.com/drive/folders/1ggt6FxBTwmMa8paDgOGQOutu4bNV26Si?usp=sharing),\nand put it under ```./datasets/```.\n\nRun on 4 GPUs:\n```shell\npython ./tools/train.py --config-file ./configs/pcct_livemouse_unet2.py # for live mouse dataset\n```\n```shell\npython ./tools/train.py --config-file ./configs/pcct_leg_unet2.py # for live leg dataset\n```\n```shell\npython ./tools/train.py --config-file ./configs/pcct_diedmouse_unet2.py # for died mouse dataset\n```\n\n### Natural Images\n\nDownload BSD68 test dataset at [here](https://drive.google.com/drive/folders/1b_RvBwIr9yLg8yPWb0BHYmWiOEVUvG4K?usp=sharing),\nand put them under the folder  ```./datasets/```\n\nPrepare dataset:\n```shell script\npython noise2sim.tools.prepare_bsd400_lmdb.py\n```\n\nRun on 1 GPU:\n```shell script\npython ./scripts/train.py --config-file ./configs/bsd400_unet2_ps3_ns8_gpu1.py # simultaneous training and testing\n```\nRun on 8 GPUs:\n```shell script\npython ./sctipts/train.py --config-file ./configs/bsd400_unet2_ps3_ns8_gpu8.py # simultaneous training and testing\n```\nThe results in the paper were obtained using 8 GPUs, you can obtain similar results with 1 GPU.\n\n## Using our pretrained models\nDownload our pretrained model [here](https://drive.google.com/drive/folders/1l9yLRBlCAo1snjiJrFhkTOPeC_lPoXc7?usp=sharing), and put these models under ```results``` folder.\nThen, run the corresponding test script as\n\n```shell\npython scripts/test_ldct.py --config-file ./configs/ldct_mayo_unet2.py # for Mayo dataset\n```\n```shell\npython scripts/test_ldct.py --config-file ./configs/ldct_fda_unet2.py # for FDA dataset\n```\n```shell\npython scripts/test_pcct.py --config-file ./configs/pcct_livemouse_unet2.py # for live mouse dataset\n```\n```shell\npython scripts/test_pcct.py --config-file ./configs/pcct_leg_unet2.py # for chicken leg dataset\n```\n```shell\npython scripts/test_pcct.py --config-file ./configs/pcct_diedmouse_unet2.py # for died mouse dataset\n```\n\n## Citation\n\n```shell\n@inproceedings{noise2sim2021,\n  title={Noise2Sim – Similarity-based Self-Learning for Image Denoising},\n  author={Niu, Chuang and Wang, Ge},\n  booktitle={arXiv:2011.03384},\n  year={2020}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniuchuangnn%2Fnoise2sim","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fniuchuangnn%2Fnoise2sim","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fniuchuangnn%2Fnoise2sim/lists"}