Resumen
Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance.