Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2023

Abstract

Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire 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 moire 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.

Keywords

video demoireing, CIDNet

Discipline

Computer Sciences | Graphics and Human Computer Interfaces

Research Areas

Software and Cyber-Physical Systems

Publication

2023 IEEE/CVF International Conference on Computer Vision (ICCV): Paris: October 2-6: Proceedings

First Page

12677

Last Page

12686

ISBN

9798350307184

Identifier

10.1109/ICCV51070.2023.01164

Publisher

IEE

City or Country

Piscataway, NJ

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/ICCV51070.2023.01164

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