Abstract
Introduction
Underwater visual sensing is severely hindered by wavelength-dependent absorption and scattering, which induce non-linear color shifts and structural haze. Existing enhancement methods often face a fundamental trade-off between chromatic restoration and geometric integrity, where color correction frequently occurs at the expense of blurring fine-grained details or amplifying noise.
Methods
A lightweight physics-inspired dual-stream network (LPD-Net) is proposed for structure-preserving enhancement, establishing an explicit mapping from physical degradation phenomena to targeted neural operators. A dual-stream architecture is designed to decouple chromatic restoration and geometric reconstruction by utilizing a raw sensor stream as a fidelity anchor and a physics-inspired prior stream for guidance. A key innovation is the spatial-difference gated fusion mechanism, which dynamically optimizes the pixel-wise weights between raw sensor observations and empirical priors through data-driven perception. By employing end-to-end joint optimization, the network adaptively suppresses prior-induced artifacts in clear water while leveraging physics-inspired guidance in turbid scenarios.
Results
The performance of the proposed model was evaluated using the UIEB and LSUI benchmarks. Experimental results indicate that LPD-Net achieves superior restoration quality, outperforming the fully aligned, competitive baseline by up to 0.67 dB in PSNR and securing the lowest perceptual distortion (0.1227 LPIPS). With a minimalist footprint of only 1.84 M parameters, the framework attains a core neural inference throughput of 27.70 FPS at 256 × 256 resolution on a mid-range GPU.
Conclusion
The proposed LPD-Net provides a robust, efficient, and high-fidelity solution for structure-preserving underwater image enhancement. Its resolution-agnostic design and competitive throughput demonstrate significant potential for real-time deployment on autonomous underwater platforms and structural survey systems.