Due to the absorption and scattering of light by water characteristics, underwater images usually present problems such as blurred details and low resolution. In order to improve the clarity of underwater images, an underwater image super-resolution reconstruction method that improves SRResNet is proposed. This method introduces the hybrid attention mechanism into the deep residual network to enhance the clarity of underwater images. Secondly, the structural similarity loss function is introduced to better protect image content, improve image quality, and make the training results more consistent with human visual perception. Experimental results show that the underwater image super-resolution reconstruction method based on improved SRResNet can effectively deal with problems such as blurred underwater images and low resolution. Compared to various other underwater image reconstruction methods on different datasets, this method improved the PSNR by 0.69 dB to 2.43 dB and the SSIM by 2.66% to 7.17%, demonstrating superior performance across all metrics.