Fast and accurate underwater image super-resolution reconstruction technology can help underwater vehicles better perceive underwater scenes and make navigation decisions. Based on this, a lightweight underwater image super-resolution reconstruction algorithm (SRIDM) based on an information distillation mechanism is proposed. Based on an ordinary residual network, the algorithm presents a global feature fusion structure, information distillation mechanism, and spatial attention module, which further enhances the feature expression ability of the model. The effectiveness of each module was validated through model ablation experiments, and the best module combination and distillation rate were discovered. The experimental results on the USR-248 test set show that the proposed algorithm restores images better than other contrast algorithms in terms of subjective visual effect and objective evaluation quality. When the magnification factor is 4, its peak signal-to-noise ratio and structural similarity reach 27.7640 dB and 0.7640 respectively. Furthermore, the proposed algorithm is also a lightweight algorithm, which significantly reduces the number of model parameters and computational complexity while maintaining performance.