Underwater Image Enhancement via HSV-CS Representation and Perception-Driven Adaptive Fusion
Fengxu Guan, Tong Guo, Yuzhu Zhang · Remote Sensing · 2026
Underwater images often suffer from color distortion and low contrast, severely limiting the reliability of visual perception systems. Existing methods struggle to balance enhancement quality and computational efficiency. To address this issue, we propose PCF-Net (Perception-driven Color Fusion Network), a lightweight dual-branch network for underwater image enhancement based on a stable HSV-CS (Hue-Saturation-Value with sine–cosine transformation) color-space representation. Specifically, a sine–cosine transformation is introduced to construct a stable HSV-CS color space, effectively avoiding hue discontinuities at boundary regions in conventional HSV representations. To compensate for underwater degradation, a Color-Bias-Aware module and a Value-Confidence module are designed to adaptively correct color distortion and luminance degradation. Furthermore, a lightweight Channel-Spatial Adaptive Gated Fusion module dynamically aggregates features from the RGB and HSV-CS branches in a perception-driven manner. The overall architecture incorporates multi-branch re-parameterizable convolutions, significantly reducing computational cost while preserving strong representational capacity. Extensive experiments on underwater image enhancement benchmarks, including UIEB and RUIE, demonstrate that PCF-Net achieves state-of-the-art performance in terms of PSNR, SSIM, and UIQM, along with visually superior color correction and contrast enhancement. With only 0.17 M parameters, the proposed model runs at 118.6 FPS on an RTX 3090 and 35.3 FPS on a Jetson Orin Nano at a resolution of 512 × 512, making it well suited for resource-constrained real-time underwater vision applications.