FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing
<|reference_start|>FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing: Moir\'e patterns are commonly seen when taking photos of screens. Camera devices usually have limited hardware performance but take high-resolution photos. However, users are sensitive to the photo processing time, which presents a hardly considered challenge of efficiency for demoir\'eing methods. To balance the network speed and quality of results, we propose a \textbf{F}ully \textbf{C}onnected en\textbf{C}oder-de\textbf{C}oder based \textbf{D}emoir\'eing \textbf{Net}work (FC3DNet). FC3DNet utilizes features with multiple scales in each stage of the decoder for comprehensive information, which contains long-range patterns as well as various local moir\'e styles that both are crucial aspects in demoir\'eing. Besides, to make full use of multiple features, we design a Multi-Feature Multi-Attention Fusion (MFMAF) module to weigh the importance of each feature and compress them for efficiency. These designs enable our network to achieve performance comparable to state-of-the-art (SOTA) methods in real-world datasets while utilizing only a fraction of parameters, FLOPs, and runtime.<|reference_end|>
@article{du2024fc3dnet:,
    title={FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing},
    author={Zhibo Du, Long Peng, Yang Wang, Yang Cao, Zheng-Jun Zha},
    journal={arXiv preprint arXiv:2406.14912},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14912},
    primaryClass={cs.CV}
}