Moire 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 moire patterns, a result of the interference between the pixel grids of the camera sensor and the device screen. Moire 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 moire patterns from photos. Since a moire 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 moire artefacts within every frequency band. We also create a large-scale benchmark data set with 1 00 000+ image pairs for investigating and evaluating moire pattern removal algorithms. Our network achieves the state-of-the-art performance on this data set in comparison to existing learning architectures for image restoration problems.