SiGGRAPH Asia 2016

Michaël Gharbi gharbi@mit.edu Gaurav Chaurasia Sylvain Paris Frédo Durand

A minimal pytorch implementation of "Deep Joint Demosaicking and Denoising" [Gharbi2016]

From this repo:

python setup.py install

Using pip:

pip install demosaicnet

Then run the demo script with:

python scripts/demosaicnet_demo.py output

To train a dummy model on the demo dataset provided, run:

python scripts/train.py --data demosaicnet/data/dummy_dataset --checkpoint_dir ckpt

To build and update the whee:

pip install wheel twine
make distribution
make upload_distribution

• How is noise handled? Where is the pretrained model? The noise-aware model is not implementation, see the earlier Caffe implementation for that https://github.com/mgharbi/demosaicnet_caffe
• How do I train this? The script scripts/train.py is a good start to setup your training job, but I haven't tested it yet, I recommend rolling your own.