GPRF-HPNet Physics-guided texture-aware fusion for real-world underwater image enhancement

Underwater images suffer from wavelength dependent attenuation and multiple scattering, which often lead to severe color casts, veiling effects, and reduced contrast. These degradations weaken the stability of key vision modules such as feature detection, feature matching, edge extraction, and object recognition, and ultimately compromise applications including visual navigation, structural inspection, and environmental monitoring for underwater robots. To improve global color consistency while preserving local texture details, we propose a Physics-Guided Texture-Aware Fusion for Real-World Underwater Image Enhancement (GPRF-HPNet). In the preprocessing stage, a YCbCr domain attenuation map is exploited to guide color correction, followed by entropy driven dual histogram global contrast enhancement. The resulting intermediate images are further combined through gradient weighted wavelet fusion, which retains structural information and fine scale details. High frequency Gabor texture maps at four orientations, namely \:0^∘\:,45^∘\:,90^∘\: and 135°, are then constructed as an explicit detail prior. These maps feed a texture branch that runs in paralle with a base branch focusing on structure and color. A parallel residual fusion unit performs joint feature extraction on the two branches, learns adaptive weights, and produces fused feature representations, after which a lightweight decoder reconstructs the enhanced image. Extensive experiments on the Color-Check7, Test-C60 and UCCS datasets demonstrate that the proposed method achieves consistent gains on six metrics, including UIQM and UCIQE and delivers more reliable performance in downstream tasks such as geometric rotation estimation and edge detection, while demonstrating strong generalization across diverse underwater scenes.