  Digital cameras and mobile phones enable us to conveniently record precious
moments. While digital image quality is constantly being improved, taking
high-quality photos of digital screens still remains challenging because the
photos are often contaminated with moir\'{e} patterns, a result of the
interference between the pixel grids of the camera sensor and the device
screen. Moir\'{e} patterns can severely damage the visual quality of photos.
However, few studies have aimed to solve this problem. In this paper, we
introduce a novel multiresolution fully convolutional network for automatically
removing moir\'{e} patterns from photos. Since a moir\'{e} pattern spans over a
wide range of frequencies, our proposed network performs a nonlinear
multiresolution analysis of the input image before computing how to cancel
moir\'{e} artefacts within every frequency band. We also create a large-scale
benchmark dataset with $100,000^+$ image pairs for investigating and evaluating
moir\'{e} pattern removal algorithms. Our network achieves state-of-the-art
performance on this dataset in comparison to existing learning architectures
for image restoration problems.
