Deep convolutional neural networks are one of the main methods for underwater image enhancement, but their expensive memory consumption and computational requirements hinder their deployment in practical applications. To this end, a lightweight dense residual convolutional neural networks (DRCNN) is proposed for underwater image enhancement. DRCNN uses depthwise separable convolution to extract high-level features to reduce computational cost; promotes information interaction between different channels through dense connection and residual learning, but also improves model representation; and fuses the input degraded image with the intermediate feature map to preserve image global similarity while preventing model gradients from vanishing. The experimental results demonstrate that DRCNN can significantly improve the quality of underwater images. When compared to the existing algorithm, DRCNN parameters are reduced by 85%, PSNR and SSIM values are increased by 3% and 2% respectively, and test speed is improved by 3%. DRCNN achieves better performance with fewer parameters, which is advantageous for real-time applications on low-resource devices.