  Due to the lack of natural scene and haze prior information, it is greatly
challenging to completely remove the haze from a single image without
distorting its visual content. Fortunately, the real-world haze usually
presents non-homogeneous distribution, which provides us with many valuable
clues in partial well-preserved regions. In this paper, we propose a
Non-Homogeneous Haze Removal Network (NHRN) via artificial scene prior and
bidimensional graph reasoning. Firstly, we employ the gamma correction
iteratively to simulate artificial multiple shots under different exposure
conditions, whose haze degrees are different and enrich the underlying scene
prior. Secondly, beyond utilizing the local neighboring relationship, we build
a bidimensional graph reasoning module to conduct non-local filtering in the
spatial and channel dimensions of feature maps, which models their long-range
dependency and propagates the natural scene prior between the well-preserved
nodes and the nodes contaminated by haze. To the best of our knowledge, this is
the first exploration to remove non-homogeneous haze via the graph reasoning
based framework. We evaluate our method on different benchmark datasets. The
results demonstrate that our method achieves superior performance over many
state-of-the-art algorithms for both the single image dehazing and hazy image
understanding tasks. The source code of the proposed NHRN is available on
https://github.com/whrws/NHRNet.
