  In the real world, the degradation of images taken under haze can be quite
complex, where the spatial distribution of haze is varied from image to image.
Recent methods adopt deep neural networks to recover clean scenes from hazy
images directly. However, due to the paradox caused by the variation of real
captured haze and the fixed degradation parameters of the current networks, the
generalization ability of recent dehazing methods on real-world hazy images is
not ideal.To address the problem of modeling real-world haze degradation, we
propose to solve this problem by perceiving and modeling density for uneven
haze distribution. We propose a novel Separable Hybrid Attention (SHA) module
to encode haze density by capturing features in the orthogonal directions to
achieve this goal. Moreover, a density map is proposed to model the uneven
distribution of the haze explicitly. The density map generates positional
encoding in a semi-supervised way. Such a haze density perceiving and modeling
capture the unevenly distributed degeneration at the feature level effectively.
Through a suitable combination of SHA and density map, we design a novel
dehazing network architecture, which achieves a good complexity-performance
trade-off. The extensive experiments on two large-scale datasets demonstrate
that our method surpasses all state-of-the-art approaches by a large margin
both quantitatively and qualitatively, boosting the best published PSNR metric
from 28.53 dB to 33.49 dB on the Haze4k test dataset and from 37.17 dB to 38.41
dB on the SOTS indoor test dataset.
