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

TL;DR

This paper introduces a novel raw domain image and video demoiréing network that leverages channel and spatial modulations, along with a new raw video dataset, to effectively remove moiré patterns caused by smartphone screen captures.

Contribution

The paper proposes a specialized raw input demoiréing network with channel and spatial modulations and introduces the first aligned raw video demoiré dataset with an efficient temporal alignment method.

Findings

Achieves state-of-the-art results in image and video demoiréing.

Demonstrates the effectiveness of raw domain processing over sRGB.

Provides a new dataset and method for raw video demoiréing.

Abstract

Capturing screen contents by smartphone cameras has become a common way for information sharing. However, these images and videos are often degraded by moir\'e patterns, which are caused by frequency aliasing between the camera filter array and digital display grids. We observe that the moir\'e patterns in raw domain is simpler than those in sRGB domain, and the moir\'e patterns in raw color channels have different properties. Therefore, we propose an image and video demoir\'eing 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 demoir\'eing (RawVDemoir\'e) 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 demori\'eing. We have released the code and dataset in https://github.com/tju-chengyijia/VD_raw.