<p>Water-related optical image enhancement (WOIE) poses a significant challenge due to the complex and variable underwater environment. Existing methods often oversimplify the underwater imaging degradation process, neglecting the effects of medium noise and target motion on image feature distribution. Additionally, reliance on reference gradients from original and synthesized ground-truth images leads networks to local optima. To address these limitations, we propose a dynamic gradient-guided network (DGNet) for WOIE. DGNet introduces a feature reconstruction and re-learning (FRR) module based on a channel combination inference (CCI) strategy and a frequency response smoothing (FRS) module. These components reduce the impact of noise and target motion on fine-grained features. During training, DGNet dynamically updates pseudo-labels with predicted images and introduces a dynamic gradient optimization space guided by white balance, enabling the network to escape saddle points and explore a broader solution space. Experiments on multiple public datasets show that DGNet outperforms the latest methods, achieving a PSNR of 25.6 dB and an SSIM of 0.929 on the UIEB dataset. In addition, extensive qualitative experiments have demonstrated that DGNet exhibits precise control over fine-grained features, highlighting its practical utility. The code will be publicly available.</p>