  Single image dehazing as a fundamental low-level vision task, is essential
for the development of robust intelligent surveillance system. In this paper,
we make an early effort to consider dehazing robustness under variational haze
density, which is a realistic while under-studied problem in the research filed
of singe image dehazing. To properly address this problem, we propose a novel
density-variational learning framework to improve the robustness of the image
dehzing model assisted by a variety of negative hazy images, to better deal
with various complex hazy scenarios. Specifically, the dehazing network is
optimized under the consistency-regularized framework with the proposed
Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the
negative information to facilitate the traditional positive-orient dehazing
objective function, by squeezing the dehazed image to its clean target from
different directions. Meanwhile, the consistency regularization keeps
consistent outputs given multi-level hazy images, thus improving the model
robustness. Extensive experimental results on two synthetic and three
real-world datasets demonstrate that our method significantly surpasses the
state-of-the-art approaches.
