Package: noise2sim
Suppression of Correlated Noises with Similarity-based Unsupervised Deep LearningIntroductionNoise2Sim is a general unsupervised deep denoising method.
On the common benchmark and practical low-dose CT datasets,
Noise2Sim performs as effectively as or even better than the supervised learning methods.
It has potential in various applications.
Details can be found on theproject pageand in thepaper.NewsJan 6, 2022, noise2sim 0.1.2 releasedDec 24, 2021, noise2sim 0.1.0 releasedInstallationPytorchis required, for example,condainstallpytorch==1.7.1torchvision==0.8.2torchaudio==0.7.2cudatoolkit=11.0-cpytorch# install pytorchThen, Noise2Sim can be easily installed through pip,pipinstallnoise2simOr Noise2Sim can be installed from source,gitclonehttps://github.com/niuchuangnn/noise2sim.gitcdnoise2sim
pipinstall-e.Train denoising model with Noise2SimLow-dose CT ImagesThe low-dose CT dataset can be obtained atLow Dose CT Grand Challenge.
The preprocessed FDA data can be obtainedhere.Arrange the Mayo data like:├── datasets   
    ├── Mayo                   
        ├── L067                    
        ├── L096
        ...List training and testing files:fromnoise2sim.toolsimportprepare_ldct
pythonprepare_ldct.py--patient-folderL067L096L109L143L192L286L291L310--output-file./datasets/Mayo/mayo_train.txt
pythonprepare_ldct.py--patient-folderL506L333--output-file./datasets/Mayo/mayo_test.txtRun on 4 GPUs:python./scripts/train.py--config-file./configs/ldct_mayo_unet2.py# for Mayo datasetpython./scripts/train.py--config-file./configs/ldct_fda_unet2.py# for FDA datasetPhoton-counting CT ImagesThe photon-counting datasets can be obtainedhere,
and put it under./datasets/.Run on 4 GPUs:python./tools/train.py--config-file./configs/pcct_livemouse_unet2.py# for live mouse datasetpython./tools/train.py--config-file./configs/pcct_leg_unet2.py# for live leg datasetpython./tools/train.py--config-file./configs/pcct_diedmouse_unet2.py# for died mouse datasetNatural ImagesDownload BSD68 test dataset athere,
and put them under the folder./datasets/Prepare dataset:pythonnoise2sim.tools.prepare_bsd400_lmdb.pyRun on 1 GPU:python./scripts/train.py--config-file./configs/bsd400_unet2_ps3_ns8_gpu1.py# simultaneous training and testingRun on 8 GPUs:python./sctipts/train.py--config-file./configs/bsd400_unet2_ps3_ns8_gpu8.py# simultaneous training and testingThe results in the paper were obtained using 8 GPUs, you can obtain similar results with 1 GPU.Using our pretrained modelsDownload our pretrained modelhere, and put these models underresultsfolder.
Then, run the corresponding test script aspythonscripts/test_ldct.py--config-file./configs/ldct_mayo_unet2.py# for Mayo datasetpythonscripts/test_ldct.py--config-file./configs/ldct_fda_unet2.py# for FDA datasetpythonscripts/test_pcct.py--config-file./configs/pcct_livemouse_unet2.py# for live mouse datasetpythonscripts/test_pcct.py--config-file./configs/pcct_leg_unet2.py# for chicken leg datasetpythonscripts/test_pcct.py--config-file./configs/pcct_diedmouse_unet2.py# for died mouse datasetCitation@inproceedings{noise2sim2021,title={Noise2Sim–Similarity-basedSelf-LearningforImageDenoising},author={Niu,ChuangandWang,Ge},booktitle={arXiv:2011.03384},year={2020}}