BEOUNet: video frame interpolation via bidirectional encoding and optimized U-Net

Video Frame Interpolation (VFI) is a technique that enhances video quality by generating new frames between adjacent video frames. However, many existing VFI methods exhibit suboptimal performance when dealing with scenes involving complex backgrounds and significant motion. In this paper, we propose an innovative VFI approach named BEOUNet to address these challenges. The model adopts a bidirectional encoding structure to comprehensively capture motion and contextual information between video frames. Furthermore, we introduce depthwise over-parameterized recurrent residual convolutional units (DO-RRCUs) and Convolutional Block Attention Module (CBAM) to optimize the U-Net architecture, thereby enhancing performance while reducing computational complexity. Finally, a multi-scale frame synthesis network is employed to generate interpolated frames. We conduct benchmark tests on various datasets, and experimental results demonstrate its excellent performance in terms of efficiency and quality.