Abstract
Video frame interpolation (VFI) is used to synthesize one or more intermediate frames between two frames in a video sequence to improve the temporal resolution of the video. However, many methods still face challenges when dealing with complex scenes involving high-speed motion, occlusions, and other factors. To address these challenges, we propose an Edge-based Multi-scale Cross Fusion Network (EMCFN) for VFI. We integrate a feature enhancement module (FEM) based on edge information into the U-Net architecture, resulting in richer and more complete feature maps, while also enhancing the preservation of image structure and details. This contributes to generating more accurate and realistic interpolated frames. At the same time, we use a multi-scale cross fusion frame synthesis model (MCFM) composed of three GridNet branches to generate high-quality interpolation frames. We have conducted a series of experiments and the results show that our model exhibits satisfactory performance on different datasets compared with the state-of-the-art methods.
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