MPD-Net: a maritime port dehazing network for structure-preserving and hue-consistent visibility restoration

This is the authors' abstract. We don't add key points for this paper.

Coastal fog and haze reduce the structural clarity, color fidelity, and visibility of images taken at coastal ports. This study proposes MPD-Net, a dual-candidate restoration network tailored for port scenes. It sequentially applies a color and luminance hint module (CLH) and a frequency-aware refinement layer (FARL) to the base output; the detail branch embeds a detail-guided feature modulation module (DGF) into the backbone network and applies CLH at the output. The dual-candidate adaptive fusion module (DCAF) fuses the two candidate results and performs color correction. On 315 paired foggy and clear port images, MPD-Net achieves the best overall PSNR, SSIM, MSE, and LPIPS among the compared approaches. Additional analyses cover scene subsets, three fogging coefficients, module contributions, and red-target preservation. For red targets, MPD-Net obtains the lowest hue MAE, whereas DehazeFormer reports lower CIEDE2000, RGB MAE, and saturation MAE. The color-related advantage is therefore specific to hue fidelity in the evaluated red regions. Future work will involve further validation using licensed real-world marine fog images and task-aware port scenarios.