Unpaired Overwater Image Defogging Using Prior Map Guided CycleGAN
This work addresses a domain-specific problem for overwater image processing, offering an incremental improvement by adapting existing CycleGAN methods with new modules for unpaired training.
The paper tackles the problem of defogging overwater images, where existing methods fail due to large sky and water expanses, and proposes a Prior map Guided CycleGAN (PG-CycleGAN) that outperforms state-of-the-art supervised, semi-supervised, and unsupervised approaches in experiments.
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.