  In this paper, 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. To the best of our knowledge, we are the first to
propose a neural approach for encoding images individually, by learning a
mapping from the 2D pixel coordinate space to the RGB color space. 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. Our code is available at
https://github.com/paubric/python-fuse-coconet.
