  Deep learning-based methods have achieved significant performance for image
defogging. However, existing methods are mainly developed for land scenes and
perform poorly when dealing with overwater foggy images, since overwater scenes
typically contain large expanses of sky and water. In this work, we propose a
Prior map Guided CycleGAN (PG-CycleGAN) for defogging of images with overwater
scenes. To promote the recovery of the objects on water in the image, two loss
functions are exploited for the network where a prior map is designed to invert
the dark channel and the min-max normalization is used to suppress the sky and
emphasize objects. However, due to the unpaired training set, the network may
learn an under-constrained domain mapping from foggy to fog-free image, leading
to artifacts and loss of details. Thus, we propose an intuitive Upscaling
Inception Module (UIM) and a Long-range Residual Coarse-to-fine framework (LRC)
to mitigate this issue. Extensive experiments on qualitative and quantitative
comparisons demonstrate that the proposed method outperforms the
state-of-the-art supervised, semi-supervised, and unsupervised defogging
approaches.
