  Single image defogging is a classical and challenging problem in computer
vision. Existing methods towards this problem mainly include handcrafted priors
based methods that rely on the use of the atmospheric degradation model and
learning based approaches that require paired fog-fogfree training example
images. In practice, however, prior-based methods are prone to failure due to
their own limitations and paired training data are extremely difficult to
acquire. Inspired by the principle of CycleGAN network, we have developed an
end-to-end learning system that uses unpaired fog and fogfree training images,
adversarial discriminators and cycle consistency losses to automatically
construct a fog removal system. Similar to CycleGAN, our system has two
transformation paths; one maps fog images to a fogfree image domain and the
other maps fogfree images to a fog image domain. Instead of one stage mapping,
our system uses a two stage mapping strategy in each transformation path to
enhance the effectiveness of fog removal. Furthermore, we make explicit use of
prior knowledge in the networks by embedding the atmospheric degradation
principle and a sky prior for mapping fogfree images to the fog images domain.
In addition, we also contribute the first real world nature fog-fogfree image
dataset for defogging research. Our multiple real fog images dataset (MRFID)
contains images of 200 natural outdoor scenes. For each scene, there are one
clear image and corresponding four foggy images of different fog densities
manually selected from a sequence of images taken by a fixed camera over the
course of one year. Qualitative and quantitative comparison against several
state-of-the-art methods on both synthetic and real world images demonstrate
that our approach is effective and performs favorably for recovering a clear
image from a foggy image.
