Deep Joint Design of Color Filter Arrays and Demosaicing

This is our Keras implementation for reconstructing test images of known datasets using our trained models.

Prerequisites

• Python 2 or 3
• Keras (with any Theano or Tensorflow backend)

Getting Started

Installation

• Install Keras along with your preferred backend. For Ubuntu, you can easily install by:

pip install tensorflow keras

• Clone this repository and enter:

git clone https://github.com/bernardohenz/deep_joint_design_cfa_demosaicing.git
cd deep_joint_design_cfa_demosaicing

Downloading our trained models

• Download trained models

python ./trained_models/download_trained_models.py

• Install h5py in order to load the models:

pip install h5py

Replicating the results from our paper

• Download test datasets

python ./datasets/download_datasets.py

• Run the test script

python test.py

• You can specify desired parameters like the following example

python test.py --datasets kodak --model our_4x4_noise --noise_std 4 --output_dir results_noise_std4

Parameter --datasets specifies the test dataset you want to evaluate ([all | kodak | mcm | hdrvdp | moire])

Parameter --model specifies the trained model to be loaded ([our_4x4_noise-free | our_4x4_noise | bayer ])

Parameter --noise_std specifies the noise std to be added to the original image (in the scale [0,255])

Parameter --output_dir specifies the folder where the reconstructions will be saved (the script will not save the reconstructions if this is not specified)

Running our models in images

• Running the specified model on a particular image

python reconstruct_image.py --img_name datasets/kodak/kodim01.png --model our_4x4_noise-free --output_name out.png

• Running the specified model on directory

python reconstruct_images_from_dir.py --dir datasets/kodak --model our_4x4_noise-free --output_dir results_kodak

Training code

The original code was in Keras v1. We have updated to work on Keras>2.0, please follow to this training repository for the training script.

Citation

If you use this code, please cite our paper

@article{HenzGastalOliveira_2018,
    author = {Bernardo Henz and Eduardo S. L. Gastal and Manuel M. Oliveira},
    title   = {Deep Joint Design of Color Filter Arrays and Demosaicing},
    journal = {Computer Graphics Forum},
    volume = {37},
    year    = {2018},
    }