We introduce NeRD, a new demosaicking method for generating full-color images from Bayer patterns. Our approach leverages advancements in neural fields to perform demosaicking by representing an image as a coordinate-based neural network with sine activation functions. The inputs to the network are spatial coordinates and a low-resolution Bayer pattern, while the outputs are the corresponding RGB values. An encoder network, which is a blend of ResNet and U-net, enhances the implicit neural representation of the image to improve its quality and ensure spatial consistency through prior learning. Our experimental results demonstrate that NeRD outperforms traditional and state-of-the-art CNN-based methods and significantly closes the gap to transformer-based methods.
@article{arxiv.2304.06566,
title = {NeRD: Neural field-based Demosaicking},
author = {Tomas Kerepecky and Filip Sroubek and Adam Novozamsky and Jan Flusser},
journal= {arXiv preprint arXiv:2304.06566},
year = {2023}
}