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Capturing screen contents by smartphone cameras has become a common way forinformation sharing. However, these images and videos are often degraded bymoir\'e patterns, which are caused by frequency aliasing between the camerafilter array and digital display grids. We observe that the moir\'e patterns inraw domain is simpler than those in sRGB domain, and the moir\'e patterns inraw color channels have different properties. Therefore, we propose an imageand video demoir\'eing network tailored for raw inputs. We introduce acolor-separated feature branch, and it is fused with the traditionalfeature-mixed branch via channel and spatial modulations. Specifically, thechannel modulation utilizes modulated color-separated features to enhance thecolor-mixed features. The spatial modulation utilizes the feature with largereceptive field to modulate the feature with small receptive field. Inaddition, we build the first well-aligned raw video demoir\'eing(RawVDemoir\'e) dataset and propose an efficient temporal alignment method byinserting alternating patterns. Experiments demonstrate that our methodachieves state-of-the-art performance for both image and video demori\'eing. Wehave released the code and dataset in https://github.com/tju-chengyijia/VD_raw.

Recaptured Raw Screen Image and Video Demoir\'eing via Channel and Spatial Modulations