Recaptured Raw Screen Image and Video Demoiréing via Channel and Spatial Modulations
This addresses a common problem for users sharing screen content via smartphone cameras, with incremental improvements in method and dataset creation.
The paper tackles moiré pattern degradation in smartphone-captured screen images and videos by proposing a demoiréing network for raw inputs, achieving state-of-the-art performance in both image and video tasks.
Capturing screen contents by smartphone cameras has become a common way for information sharing. However, these images and videos are often degraded by moiré patterns, which are caused by frequency aliasing between the camera filter array and digital display grids. We observe that the moiré patterns in raw domain is simpler than those in sRGB domain, and the moiré patterns in raw color channels have different properties. Therefore, we propose an image and video demoiréing network tailored for raw inputs. We introduce a color-separated feature branch, and it is fused with the traditional feature-mixed branch via channel and spatial modulations. Specifically, the channel modulation utilizes modulated color-separated features to enhance the color-mixed features. The spatial modulation utilizes the feature with large receptive field to modulate the feature with small receptive field. In addition, we build the first well-aligned raw video demoiréing (RawVDemoiré) dataset and propose an efficient temporal alignment method by inserting alternating patterns. Experiments demonstrate that our method achieves state-of-the-art performance for both image and video demoriéing. We have released the code and dataset in https://github.com/tju-chengyijia/VD_raw.