Single Sea Surface Image Dehazing via Multi-scale Concatenated Attention Network

Abstract

Haze degrades obscures and content information of sea surface images, which can negatively impact the navigation safety of the intelligent ship in real-time systems. Therefore, it is imperative to develop algorithms that effectively remove haze and make the intelligent ship have higher environmental adaptability. In this paper, we propose a novel Multi-scale Concatenated Attention Network (MCA-Net) to address this problem. Specifically, we use the concatenation operation to connect multi-scale residual blocks (MRB), while introducing the channel attention mechanism for guiding the hazy region feature extracting. Extensive experiments on both synthetic datasets and real sea surface images, which demonstrate our proposed method outperforms are better than other recent dehazing approaches.