Underwater images often suffer from color distortion, reduced contrast, and blurriness due to light refraction, absorption, and scattering. In this paper, we propose a coarse-to-fine deepPyramid network forUnderwaterImageEnhancement (PyUIE). Specifically, PyUIE begins by decomposing the input image into high- and low-frequency components using a Laplacian pyramid. The low-frequency residual, which primarily contains lighting and color information, is processed with a lightweight deterministic color mapping network to correct global illumination and color distortions. Concurrently, the high-frequency components containing the fine details are enhanced in a coarse-to-fine manner, such that each higher scale is guided by the reconstruction from the adjacent lower scale. This hierarchical strategy effectively mitigates the risk of over-enhancement by avoiding excessive modifications to the high-frequency components. Additionally, we implement a multi-scale supervised training strategy, enabling the model to learn and reconstruct features across multiple scales, which enhances its ability to capture diverse details and improves its generalization and robustness. Extensive experiments demonstrate that our method successfully restores fine details and small structures in underwater images while producing vivid and visually appealing colors, thereby outperforming existing enhancement methods in both qualitative and quantitative evaluations. The code is available athttps://github.com/ttttllt/PyUIE.git.