Thesis-Denoising-autoencoder

Denoising with autoencoder

Description

Utilisation d'un autoencoder pour apprendre statistiquement comment il est possible de générer une image de synthèse.

Input :

• Noisy image
• Z-buffer
• Normal card

or other information...

Output :

• Reference image

Requirements

git clone --recursive https://github.com/prise-3d/Thesis-Denoising-autoencoder.git XXXXX

pip install -r requirements.txt

How to use ?

Autoencoder keras documentation

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

python generate/generate_reconstructed_data.py -h

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

python generate/generate_dataset.py -h

Reconstruction parameter (--params)

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: "30, 35"
• fast_ica_reconstruction: Fast Iterative Component Analysis
  • Param definition: number of components used for compression
  • Example: "50"
• static Use static file to manage (such as z-buffer, normals card...)
  • Param definition: Name of image of scene need to be in {sceneName}/static/xxxx.png
  • Example: "img.png"

Example:

python generate/generate_dataset.py --output data/output_data_filename --metrics "svd_reconstruction, ipca_reconstruction, fast_ica_reconstruction" --renderer "maxwell" --scenes "A, D, G, H" --params "100, 200 :: 50, 10 :: 50" --nb_zones 10 --random 1 --only_noisy 1

Then, run the model:

python image_denoising --data data/my_dataset --output output_model_name

License

The MIT license