Vision Transformer-Based Image Dehazing for Climate-Resilient Maritime Navigation

As climate change intensifies, maritime transportation systems face critical challenges from frequent fog, high humidity, and adverse weather, which degrade visual perception and threaten navigation safety. To address these climate-induced disruptions, we propose TWRM-Net, a Transformer-based dehazing network designed for climate-resilient maritime vision. Firstly, the network employs a hierarchical encoder–decoder backbone that integrates convolutional priors with window-based self-attention, enabling joint modeling of local textures and global haze structures for maritime haze removal. Secondly, we introduce a Dual-Residual Attention Block (DRAB), which enhances structural awareness and haze localization by…

• NN
  National Natural Science Foundation of China

  Awards: 52372316, 52472347, 52331012