A CONTRASTIVE LEARNING APPROACH FOR SCREENSHOT DEMOIRÉING

We propose a contrast learning-based approach for screenshot demoiréing based on the assumption that a moiré image can be separated into two layers in deep latent space: moiré artifacts and latent clean image. First, we develop a multiscale network, called SDN, that extracts multiscale feature maps of an input image and then separates them into moiré and clean image components. To improve the separation of the features, we develop a contrast learning approach that separates and clusters moiré and clean image features in the latent space in supervised and unsupervised manners, respectively. Experimental results on a misaligned real-world screenshot dataset show that the proposed algorithm provides better demoiréing performance than state-of-the-art algorithms.