We propose a deep neural network approach for mapping the 2D pixel coordinates in an image to the corresponding Red-Green-Blue (RGB) color values. The neural network is termed CocoNet, i.e. COordinates-to-COlor NETwork. During the training process, the neural network learns to encode the input image within its layers. More specifically, the network learns a continuous function that approximates the discrete RGB values sampled over the discrete 2D pixel locations. At test time, given a 2D pixel coordinate, the neural network will output the approximate RGB values of the corresponding pixel. By considering every 2D pixel location, the network can actually reconstruct the entire learned image. It is important to note that we have to train an individual neural network for each input image, i.e. one network encodes a single image only. Our neural image encoding approach has various low-level image processing applications ranging from image encoding, image compression and image denoising to image resampling and image completion. We conduct experiments that include both quantitative and qualitative results, demonstrating the utility of our approach and its superiority over standard baselines, e.g. bilateral filtering or bicubic interpolation.
The demontration script are written Python 3 using Keras with Tensorflow back-end, along with other utility libraries.
Install Python 3.
sudo apt-get install python3.6
Install TKinter.
apt-get install python-tk
Install python module requirements from provided text file.
pip install -r requirements.txt
Run test file.
python3 test.py
Install Python 3 and TKinter. Install python module requirements from provided text file.
pip install -r requirements.txt
Run test file.
python3 test.py
Install Docker Build Docker image.
sudo make bash GPU=0
Install additional requirements.
apt-get install python-tk
Clone repository. Install python module requirements from provided text file.
pip install -r requirements.txt
Run test file.
python3 test.py
Please cite the following work if you use any part of this code in your scientific work:
@inproceedings{ Bricman-ICONIP-2018,
authors = {Paul Andrei Bricman and Radu Tudor Ionescu},
title = "{CocoNet: A deep neural network for mapping pixel coordinates to color values}",
booktitle = {Proceedings of ICONIP},
year = {2018}}