Moir'e Photo Restoration Using Multiresolution Convolutional Neural Networks

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.