MFNet:Real-Time Motion Focus Network for Video Frame Interpolation

作者：Guosong Zhu, Zhen Qin, Yi Ding, Yao Liu, Zhiguang Qin · 发表于：IEEE Transactions on Multimedia · 年份：2023 · DOI：10.1109/tmm.2023.3308442 · 被引用次数：7 · 研究领域：Advanced Vision and Imaging、Advanced Image Processing Techniques、Image Processing Techniques and Applications

As a popular research topic in computer vision, video frame interpolation is widely used in video processing tasks. However, this task is often limited by slow processing speed or high memory consumption in practical applications. To address these drawbacks, a frame interpolation network focusing on motion regions named MFNet is proposed, which consists of a sampler for adaptive and efficient separation of motion regions from the background, a fine-grained module for direct approximation of intermediate streams, and a lightweight module for bi-directional optical stream fusion. Extensive experiments show that our MFNet achieves optimal accuracy on some frame interpolation tasks and is much faster than other state-of-the-art methods. In addition, transplantation of the core components of MFNet to other frame interpolation networks can significantly improve the performance.