The quality of underwater videos depends on absorption and scattering of light, as well as on the performance of transmission devices. In this paper, we leverage the characteristics of underwater imaging and develop a video super-resolution method optimized for underwater scenarios. We first construct a video dataset containing a substantial amount of authentic underwater footage for experimental analysis. Then, we preprocess underwater images based on the principle of minimal color loss, mitigating color distortions caused by lighting effects in underwater videos. Moreover, we incorporate a residual network with spatial attention mechanisms to align features from both reference and adjacent frames, thereby obtaining richer information. Finally, by merging linearly upsampled reference frames with features aligned using spatial attention, we reconstruct high-resolution videos at their original resolution. We evaluate the proposed method both qualitatively and quantitatively on the created underwater dataset. The results demonstrate that our method effectively addresses light attenuation in underwater settings, leading to improved quality in super-resolution reconstruction.