Underwater image enhancement (UIE) aims to mitigate wavelength-dependent absorption and multi-path scattering effects, enabling the recovery of natural colors and rich details. Despite notable progress, consistently achieving high-quality enhancement in both fidelity and perceptual clarity remains a fundamental challenge. To address this, we propose the Laplacian domain Dual-Focus Enhancer (DFE), an innovative framework consisting of two stages: adaptive diffusion-accelerated low frequency enhancement (ADALE) and progressive uncertainty driven high-frequency enhancement (PUHE). Specifically, DFE applies a Laplacian transform to decouple the frequency-specific degradations in underwater images, supporting fidelity- and clarity-oriented enhancement along separate pathways. To facilitate high-fidelity restoration, ADALE incorporates an HSV guided optimization mechanism (HSV-OM) to establish a robust color and brightness calibration baseline for the low-frequency diffusion model, adaptively managing basic degradations with minimal sampling steps. Furthermore, to enhance contour and detail perception, PUHE models the uncertainty of reference textures and integrates it with feature modulation to progressively reconstruct multi-scale high-frequency structures. The multi reference underwater texture enhancement (MUTE) dataset fur ther improves image clarity. Extensive experiments demonstrate that our DFE outperforms state-of-the-art (SOTA) methods in both quantitative metrics and visual quality.