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

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

Learning Image Demoireing from Unpaired Real Data

This paper focuses on addressing the issue of image demoireing. Unlike the large volume of existing studies that rely on learning from paired real data, we attempt to learn a demoireing model from unpaired real data, i.e., moire images associated with irrelevant clean images. The proposed method, referred to as Unpaired Demoireing (UnDeM), synthesizes pseudo moire images from unpaired datasets, generating pairs with clean images for training demoireing models. To achieve this, we divide real moire images into patches and group them in compliance with their moire complexity. We introduce a novel moire generation framework to synthesize moire images with diverse moire features, resembling real moire patches, and details akin to real moire-free images. Additionally, we introduce an adaptive denoise method to eliminate the low-quality pseudo moire images that adversely impact the learning of demoireing models. We conduct extensive experiments on the commonly-used FHDMi and UHDM datasets. Results manifest that our UnDeM performs better than existing methods when using existing demoireing models such as MBCNN and ESDNet-L. Code: https://github.com/zysxmu/UnDeMComment: AAAI202