Abstract: In the imaging process of approach detection, dynamic image sequences often have problems such as image blur and insufficient resolution due to platform movement and jitter. This paper studies the super-resolution of image sequences in the process of approach detection and proposes a video super-resolution method based on BasicVSR++. By introducing spatial and channel attention mechanisms to enhance the model’s ability to extract detail features, combined with shared projection weights, multi-group mechanisms and sampling point modulation, the effect of the alignment module is improved. While improving the network feature extraction capability, it makes up for the shortcomings of regular convolution in long-distance dependency and adaptive spatial aggregation. At the same time, downsampling is combined with a low-pass filter to reduce the high-frequency components of the image, which improves the robustness of the model to slight image jitter. In addition, a new upsampling module is introduced to combine local and global features, generate an adaptive upsampling kernel to expand the receptive field, and better restore the global structure and reconstruct details. The simulation experimental results show that the proposed method improves the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) indicators by 2.2% and 2.1% respectively compared with the original method, which proves the effectiveness of the method proposed in this paper in improving the quality super-resolution reconstruction of the image sequence in close proximity.