  Interference between overlapping gird patterns creates moire patterns,
degrading the visual quality of an image that captures a screen of a digital
display device by an ordinary digital camera. Removing such moire patterns is
challenging due to their complex patterns of diverse sizes and color
distortions. Existing approaches mainly focus on filtering out in the spatial
domain, failing to remove a large-scale moire pattern. In this paper, we
propose a novel model called FPANet that learns filters in both frequency and
spatial domains, improving the restoration quality by removing various sizes of
moire patterns. To further enhance, our model takes multiple consecutive
frames, learning to extract frame-invariant content features and outputting
better quality temporally consistent images. We demonstrate the effectiveness
of our proposed method with a publicly available large-scale dataset, observing
that ours outperforms the state-of-the-art approaches, including ESDNet,
VDmoire, MBCNN, WDNet, UNet, and DMCNN, in terms of the image and video quality
metrics, such as PSNR, SSIM, LPIPS, FVD, and FSIM.
