Current Video Super-Resolution (VSR) algorithms cannot fully utilize inter-frame information of different distances when processing complex scenes with large motion amplitude, resulting in difficulty in accurately recovering occlusion, boundaries, and multi-detail regions. A VSR model based on frame straddling optical flow was proposed to solve these problems. Firstly, shallow features of Low-Resolution frames (LR) were extracted through Residual Dense Blocks (RDBs). Then, motion estimation and compensation was performed on video frames using a Spatial Pyramid Network (SPyNet) with straddling optical flows of different time lengths, and deep feature extraction and correction was performed on inter-frame information through RDBs connected in multiple layers. Finally, the shallow and deep features were fused, and High-Resolution frames (HR) were obtained through up-sampling. The experimental results on the REDS4 public dataset show that compared with deep Video Super-Resolution network using Dynamic Upsampling Filters without explicit motion compensation (DUF-VSR), the proposed model improves Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) by 1.07 dB and 0.06, respectively. The experimental results show that the proposed model can effectively improve the quality of video image reconstruction.