Underwater image enhancement is challenging due to the complex and variable degradations induced by environmental disturbances. While deep learning methods recently have achieved substantial progress in image enhancement, their performance is still hampered by the limited understanding of underwater image characteristics. To address this, we propose the hierarchical decomposition-based underwater image enhancement network (HDNet), which tackles key elements of underwater image degradation. Our model employs macroscopic channel decomposition and microscopic pixel decomposition to correct color distortion from light attenuation and restore detail loss from scattering. In the pixel decomposition process, HDNet interprets pixels as perturbed light signals and refines image details by representing and modulating these signals, emulating the principles of the Fourier transform. In addition, we introduce an auxiliary gradient guidance strategy to mitigate the effects of poor reference images during training. HDNet demonstrates good performance across multiple datasets. Notably, on the underwater image enhancement dataset, a real-world underwater dataset, HDNet achieves a peak signal-to-noise ratio of 24.158 dB and a structural similarity index of 0.923, outperforming many previous state-of-the-art models. Its low parameter count and computational efficiency make it suitable for practical, real-world applications.