  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.
