demosaicnet 0.0.14

Minimal implementation of Deep Joint Demosaicking and Denoising [Gharbi2016]

# Deep Joint Demosaicking and Denoising SiGGRAPH Asia 2016

Michaël Gharbi [e-mail] Gaurav Chaurasia Sylvain Paris Frédo Durand

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

# Installation

From this repo:

`shell python setup.py install `

Using pip:

`shell pip install demosaicnet `

Then run the demo script with:

`shell python scripts/demosaicnet_demo.py output `

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

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

To build and update the whee:

`shell pip install wheel twine make distribution make upload_distribution `

# FAQ

• 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.