Deep Video Demoiréing via Compact Invertible Dyadic Decomposition.

Removing moiré patterns from videos recorded on screens or complex textures is known as video demoiréing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moiré patterns and can be significantly erased in the removal process. By interpreting video demoiréing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moiré patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moiré patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the network’s output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.