Syntesis images noise detection using CNN approach

2 Ramas

Jérôme BUISINE 0d651ec858 Create generate dataset 3D		hace 5 años
modules	6415e68e47 Update of data augmentation	hace 5 años
.gitignore	7b3768132f Add of new reconstruction metric	hace 5 años
LICENSE	f80a4942e7 Creation of dataset file script added	hace 5 años
README.md	0d651ec858 Create generate dataset 3D	hace 5 años
generate_dataset.py	0d651ec858 Create generate dataset 3D	hace 5 años
generate_dataset_3D.py	0d651ec858 Create generate dataset 3D	hace 5 años
generate_reconstructed_data.py	6415e68e47 Update of data augmentation	hace 5 años
predict_noisy_image_svd.py	0d651ec858 Create generate dataset 3D	hace 5 años
predict_seuil_expe_maxwell_curve.py	0d651ec858 Create generate dataset 3D	hace 5 años
requirements.txt	153ef98eae Update code with use of IPFML package	hace 6 años
run.sh	0d651ec858 Create generate dataset 3D	hace 5 años
run_maxwell_simulation_custom.sh	0d651ec858 Create generate dataset 3D	hace 5 años
run_test.sh	0d651ec858 Create generate dataset 3D	hace 5 años
train_model.py	0d651ec858 Create generate dataset 3D	hace 5 años
transformation_functions.py	7b3768132f Add of new reconstruction metric	hace 5 años

Noise detection project

Requirements

pip install -r requirements.txt

Generate reconstructed data from specific method of reconstruction (run only once time or clean data folder before):

python generate_reconstructed_data.py -h

Generate custom dataset from one reconstructed method or multiples (implemented later)

python generate_dataset.py -h

List of expected parameter by reconstruction method:

svd_reconstruction: Singular Values Decomposition
- Param definition: interval data used for reconstruction (begin, end)
- Example: "100, 200"
ipca_reconstruction: Iterative Principal Component Analysis
- Param definition: number of components used for compression and batch size
- Example: "50, 32"
fast_ica_reconstruction: Fast Iterative Component Analysis
- Param definition: number of components used for compression
- Example: "50"

This project contains modules:

modules/utils/config.py: Store all configuration information about the project and dataset information
modules/utils/data.py: Usefull methods used for dataset

All these modules will be enhanced during development of the project