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.
翻译:深层学习方法在图像脱色方面取得了显著的性能,但是,现有方法主要是为陆地场景开发的,在处理水面雾化图像时表现不佳,因为水面场景通常包含大片天空和水。在这项工作中,我们提议使用一个前地图导导环GAN(PG-CycleGAN),用水面图像脱色。为了促进图像中水面物体的恢复,为网络开发了两个丢失功能,先用一张地图来反向暗色通道,并用微量成形法正常化来压制天空和强调天体。然而,由于未设防雾化的成套培训,该网络可能从雾到无雾的图像中学习一个控制不足的域图,导致人工制品和细节的丢失。因此,我们提议用一个直觉放大感应模块和一个长程残余孔对底线框架(LRC)来减轻这一问题。关于定性和定量比较的大规模实验表明,拟议的方法超越了州级、监督的半级和节制方法。