  Despite their remarkable expressibility, convolution neural networks (CNNs)
still fall short of delivering satisfactory results on single image dehazing,
especially in terms of faithful recovery of fine texture details. In this
paper, we argue that the inadequacy of conventional CNN-based dehazing methods
can be attributed to the fact that the domain of hazy images is too far away
from that of clear images, rendering it difficult to train a CNN for learning
direct domain shift through an end-to-end manner and recovering texture details
simultaneously. To address this issue, we propose to add explicit constraints
inside a deep CNN model to guide the restoration process. In contrast to direct
learning, the proposed mechanism shifts and narrows the candidate region for
the estimation output via multiple confident neighborhoods. Therefore, it is
capable of consolidating the expressibility of different architectures,
resulting in a more accurate indirect domain shift (IDS) from the hazy images
to that of clear images. We also propose two different training schemes,
including hard IDS and soft IDS, which further reveal the effectiveness of the
proposed method. Our extensive experimental results indicate that the dehazing
method based on this mechanism outperforms the state-of-the-arts.
