  Recently, there has been rapid and significant progress on image dehazing.
Many deep learning based methods have shown their superb performance in
handling homogeneous dehazing problems. However, we observe that even if a
carefully designed convolutional neural network (CNN) can perform well on
large-scaled dehazing benchmarks, the network usually fails on the
non-homogeneous dehazing datasets introduced by NTIRE challenges. The reasons
are mainly in two folds. Firstly, due to its non-homogeneous nature, the
non-uniformly distributed haze is harder to be removed than the homogeneous
haze. Secondly, the research challenge only provides limited data (there are
only 25 training pairs in NH-Haze 2021 dataset). Thus, learning the mapping
from the domain of hazy images to that of clear ones based on very limited data
is extremely hard. To this end, we propose a simple but effective approach for
non-homogeneous dehazing via ensemble learning. To be specific, we introduce a
two-branch neural network to separately deal with the aforementioned problems
and then map their distinct features by a learnable fusion tail. We show
extensive experimental results to illustrate the effectiveness of our proposed
method.
