<p>Underwater imaging faces challenges such as light scattering and color distortion, which result in blurred details, unclear edges, and inaccurate colors. Enhancing the quality of underwater images and videos is essential for improving marine visibility, enabling better exploration of marine resources, and supporting biodiversity conservation through advanced computer vision techniques. Existing methods often struggle to maintain structural clarity, reduce noise and haze, and ensure consistent color representation. To overcome these limitations, there is a need for more advanced and adaptive approach that can handle the dynamic and complex nature of underwater environments. In light of this, we propose a framework that incorporates a Color Degradation Prompt Generation Block (CDPG) which extracts and leverages color degradation information as queries, enabling the generation of output features specifically tailored to the color characteristics of underwater imagery. Further, we propose a Prompt-Aware Query Modulation Block (PAQM) that establishes color degradation prompts as queries, producing output features that dynamically respond to the specific color distortion characteristics identified in underwater images. Several experiments on underwater image enhancement datasets verify the effectiveness of the proposed method.</p>