Multiscale Coarse-to-Fine Guided Screenshot Demoiréing

In this letter, we propose a multiscale coarse-to-fine guided screenshot demoiréing algorithm. We first extract the multiscale features of the input image. Then, we develop the multiscale guided restoration block (MGRB), which removes moiré patterns with the guidance of multiscale information by exploiting the correlation between moiré frequencies. To this end, we design two blocks for feature modulation and moiré pattern removal. In addition, to further improve the performance, we develop an adaptive reconstruction loss to direct the network to focus on regions that are difficult to restore. Experimental results on multiple datasets demonstrate that the proposed algorithm provides comparable or even better demoiréing performance than state-of-the-art algorithms.