Clear visual information is indispensable for tasks such as autonomous navigation, ecological monitoring, and inspection in marine environments, yet underwater images are notoriously marred by colour casts, haze, and loss of detail. We present DRM-Net, a practical enhancement framework that turns this challenge into an explicit recovery problem. Instead of guessing the whole clean image, DRM-Net first predicts a Degradation Residual Map (DRM) that pinpoints, pixel by pixel, how much colour, contrast, and texture have been lost. Adding this residual back to the raw frame produces the restored result in a single, transparent step. A lightweight Subaquatic Multi-Scale Context Fusion module further enhances robustness by allowing the network to view the scene through multiple “water layers”, adaptively selecting the most relevant scale for each image. Guided jointly by a DRM residual L1-loss and a perceptual loss, DRM-Net delivers sharper edges and truer colours while adding only negligible computational overhead. Comprehensive experiments on benchmark datasets demonstrate that our method effectively restores underwater images with superior colour fidelity, perceptual quality, and structural details. Compared with state-of-the-art approaches, our framework achieves significant improvements in both quantitative metrics and qualitative visual assessments across diverse underwater scenarios.