Cross-Scale Style-Guided Enhancement for Underwater Remote Sensing Imagery

Underwater imaging is essential for marine remote sensing tasks, such as environmental monitoring, resource exploration, and autonomous navigation. However, images captured in underwater environments often suffer from complex degradations, including wavelength-dependent color distortion, contrast attenuation, and structural detail loss. To address these challenges, we propose a cross-scale style-guided network (CSG-Net) for robust underwater image enhancement (UIE). CSG-Net employs a dual-stage collaborative framework that decouples global degradation modeling from local detail refinement. In the first stage, a style extraction network (SE-Net) extracts multiscale degradation-aware style priors that implicitly encode large-scale physical degradation patterns, such as red-channel attenuation and spectral imbalance. In the second stage, a style-guided enhancement network (SG-Net) leverages these style features to guide spatially adaptive enhancement, enabling consistent color correction and fine-grained detail recovery. To alleviate semantic degradation during scale transitions, CSG-Net introduces the proposed multiresolution feature-preserving cross-scale interaction (MFPCSI) module, which enhances the preservation and integration of hierarchical features. Combined with the multistream information fusion (MSIF) module, this design enables the effective fusion of semantic and structural information across spatial scales. The proposed components enable the preservation of fine-grained details while adaptively integrating semantic and structural cues across multiple scales. Comprehensive experiments conducted on diverse and challenging underwater image datasets demonstrate that CSG-Net consistently surpasses state-of-the-art approaches in terms of the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM), and the underwater image quality measure (UIQM). Furthermore, the model exhibits strong cross-domain generalization and delivers high-fidelity visual results, underscoring its suitability for deployment in practical vision systems operating under complex, real-world environments.