DCD-UIE: Decoupled Chromatic Diffusion Model for Underwater Image Enhancement

Color distortion and structural degradation in underwater images are classic challenges in underwater image enhancement. The core goal is to restore degraded images to high-quality images with both color and structure that conform to visual perception. However, in the traditional RGB space, these two issues are highly coupled, resulting in existing enhancement methods often neglecting one over the other. To address this challenge, we propose a guided diffusion model based on the principle of decoupling. Our key insight is that in perceptual color spaces such as HSV, color (H, S) and structure (V) are naturally separated. To exploit this property, we first design an adaptive perceptual guidance module, which analyzes the degraded HSV image and generates two orthogonal guidance signals: a color guide and a structure guide, which guide the denoising process of the diffusion model. To ensure that this decoupled guidance is faithfully implemented, we propose a corresponding decoupled loss optimization module, which uses independent loss functions to supervise the final output color and structure. By combining the forward decoupled guidance with the backward decoupled supervision, we construct a closed-loop optimization framework. This framework enables the model to collaboratively optimize color and structure under various degradation scenarios. Extensive experiments demonstrate that our proposed method outperforms existing state-of-the-art approaches in a variety of underwater scenes, particularly those degraded by color casts and haze. Furthermore, it exhibits superior performance on no-reference image quality assessment metrics. The source code is available at https://github.com/zy-world/DCD-UIE.