A maritime image dehazing algorithm based on improved VQGAN
Zhixin Yuan, Lining Zhao, Shukai Tian, Dong Zhang · Ships and Offshore Structures · 2024
Maritime image dehazing faces challenges as existing methods struggle to handle different concentrations of sea fog, and the treatment of details in dehazed results is often suboptimal. This paper proposes a maritime image dehazing algorithm based on the VQGAN mode. Specifically, since VQGAN is capable of generating high-quality discrete codebooks, we input the haze-free maritime dataset into VQGAN for pre-training to obtain a high-quality codebook prior. During the vector quantisation stage,high-quality vectors from the discrete codebook undergo distance calculations with the upsampled vectors containing haze.This process replaces the vectors containing haze with the high-quality vectors. To address the issue of residual fog, the authors introducing the FAM-UNet encoder.To tackle distortion issues, the authors introduced the Pyramid, Cascading, and Deformable Convolution Feature Alignment Module (PCD) into the original model. The experimental results indicate that our model can effectively restore clear images, providing assistance for subsequent advanced computer vision tasks..