Multi-scale video demoireing based on selective time-domain fusion

Video demoiréing aims to remove Moiré patterns from video footage captured by cameras when recording electronic displays. Considering the limitation that existing methods often fail to effectively exploit temporal information across video frames, thereby hindering their ability to recover details and maintain temporal consistency, this letter proposed a novel Multi-scale Video Demoiréing network (MVDS) based on Selective Temporal Fusion. By employing deformable convolutions for frame alignment and a selective temporal fusion mechanism, MVDS efficiently exploits temporal information to improve demoiréing performance. An embedded multi-scale U-net with attention fusion further refines image details at different scales. Extensive experiments on the VDMoiré dataset demonstrate that MVDS outperforms state-of-the-art methods in terms of both qualitative and quantitative metrics, effectively removing Moiré patterns while preserving image details and temporal consistency.