A Dual‐Stage Frequency‐Driven Network for Texture and Structure‐Aware Underwater Image Enhancement

ABSTRACT Underwater images often suffer from color distortion, texture degradation, and structural blurring due to wavelength‐dependent absorption and scattering. To address these issues, we propose FD‐DMTNet, a novel two‐stage enhancement framework that integrates frequency‐domain priors with fine‐grained structural refinement. In the first stage, a frequency‐aware U‐Net is built using Frequency‐Domain Correction Blocks (FDCB) and Multi‐Scale Feature Stream Blocks (MSFS), while a Frequency‐Domain Transformer (FETB) with multi‐head self‐attention enables global context learning. In the second stage, a Fine‐Grained Enhancement Module (FGEN) comprising three branches is introduced: A Texture Enhancement Branch (TEB) for multiscale texture recovery, a Color Correction Branch (CCB) for frequency‐guided color adjustment, and a Structure Refinement Branch (SRB) using edge‐aware attention and FETB to restore structural details. Extensive experiments on multiple benchmark datasets demonstrate that FD‐DMTNet significantly outperforms existing methods in terms of color accuracy, texture clarity, and structural consistency. Compared with state‐of‐the‐art approaches, it achieves average improvements of 3.66%, 2.04%, 2.48%, and 1.83% in PSNR, SSIM, UIQM, and NIQE, respectively.