Recaptured Screen Image Demoiréing

In many situations, such as transferring data between devices and recording precious moments, we would like to capture the contents on screens using digital cameras for convenience. These recaptured screen images and videos suffer from a special type of degradation called “moiré pattern”, which is caused by the aliasing between the grid of display screen and the array of camera sensor. However, few works are proposed to tackle this problem. Considering the great success of convolutional neural networks (CNNs) in image restoration, we propose a CNN-based moiré removal method for recaptured screen images. There are mainly two contributions in this paper. First, for the generation of training data, we propose an image registration algorithm via global homography transform and local patch matching to compensate the significant viewpoint disparity between the recaptured screen image and the moiré-free image obtained via screenshot. We construct a moiré removal and brightness improvement (MRBI) database with aligned moiré-free and moiré images. Second, we propose a convolutional neural Network with Additive and Multiplicative modules (termed as AMNet) to transfer the low light moiré image to the bright moiré-free image. The proposed network is trained with pixel-wise loss, perceptual loss, and adversarial loss. Extensive experiments on 340 test images demonstrate that the proposed method outperforms state-of-the-art moiré removal methods.