FC3DNet: A Fully Connected Encoder-Decoder for Efficient Demoir'eing
This addresses the need for fast demoiréing on camera devices with limited hardware, though it appears incremental as it builds on existing encoder-decoder approaches.
The paper tackles the problem of efficiently removing moiré patterns from photos of screens, proposing FC3DNet, which achieves performance comparable to state-of-the-art methods while using significantly fewer parameters, FLOPs, and runtime.
Moiré 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 demoiréing 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}emoiréing \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 moiré styles that both are crucial aspects in demoiréing. 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.