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Use of GAN approach in order to generate monte carlo noise applied on synthesis images

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Jérôme BUISINE 714bc0055b Merge branch 'release/v0.0.2' 5 anni fa
synthesis_images 6901223dae Update of data; Save and load option added 5 anni fa
.gitignore a003b62672 Update of documentation 5 anni fa
Dockerfile b89725ffd3 Add of gan for image synthesis 6 anni fa
LICENSE a003b62672 Update of documentation 5 anni fa
Makefile b89725ffd3 Add of gan for image synthesis 6 anni fa
README.md a003b62672 Update of documentation 5 anni fa
ganAtariImage.py b89725ffd3 Add of gan for image synthesis 6 anni fa
ganSynthesisImage.py 70d4356303 try using 200 pixels images 6 anni fa
ganSynthesisImage_100.py 51f1999d4f Add of gan for 100x100 images 5 anni fa
ganSynthesisImage_200.py 6901223dae Update of data; Save and load option added 5 anni fa
noise_gan.ipynb 51f1999d4f Add of gan for 100x100 images 5 anni fa
prepare_data.py 6901223dae Update of data; Save and load option added 5 anni fa
requirements.txt b89725ffd3 Add of gan for image synthesis 6 anni fa
tensorboard.ipynb 51f1999d4f Add of gan for 100x100 images 5 anni fa

README.md

Noise Generation

Description

Study of how to generate noise filter from monte carlo rendering in synthesis images using Generative Adversarial Network approach. The aim of this project is to reproduce monte carlo noise obtained during rendering.

How to use ?

pip install -r requirements.txt

python prepare_data.py

python ganSynthesisImage_200.py

Contributors

License

MIT