High Resolution Demoire Network

Photographing LCD screens for information recording has become a prevalent behavior, but moiré artifact will appear in screen-shot pictures with different colors and shapes. In order to remove such artifacts, previous work mostly used multi-scale framework to identify the complex frequencies of moiré, but the relationship between different scales used to be ignored. In this paper, we proposed a novel High-Resolution Demoiré Network(HRDN) to fully explore the relationship between feature maps with different resolutions. It consists of three main components: parallel high resolution network, continuous information exchange modules and final feature fusion layer. Extensive experiments demonstrate our method outperforms the state-of-art both in quantity and quality, the PSNR of demoiré images restored by HRDN reached 28.47, which is 0.72 higher than MopNet [1]. To facilitate research in image demoiréing we have published our code in github.