Lightweight Visual Enhancement for Reliable Vision Systems in Adverse Port Weather

Abstract

Vision-based perception systems are central to monitoring, inspection, and safety assurance in smart ports. However, adverse weather, particularly rain and fog, degrades image quality, suppresses structural details, and impairs downstream visual perception. Meanwhile, practical port vision systems are often deployed on resource-constrained edge platforms, imposing strict requirements on computational efficiency. To address these challenges, we propose PortVE, a lightweight visual enhancement framework for adverse port weather conditions. PortVE is built on an encoder-decoder architecture and combines degradation-aware Multi-Scale Pooling with Pool-Conv Downsampling and Frequency Directional Modulation. This design captures coarse-to-fine degradation cues, directional structures, and frequency-domain information while maintaining a compact computational profile. Experiments on public benchmarks and a self-collected port-scene synthetic weather dataset demonstrate that PortVE achieves strong restoration performance and efficient inference. It achieves 37.64 dB PSNR on SOTS outdoor and 34.21 dB PSNR on Test2800 with 6.17 M parameters and 42.83 GFLOPs. Downstream object detection experiments further demonstrate that PortVE improves detection robustness under adverse weather. The source code is publicly available at https://github.com/jssc-ZhaoBG/PortVE