  The formulation of the hazy image is mainly dominated by the reflected lights
and ambient airlight. Existing dehazing methods often ignore the depth cues and
fail in distant areas where heavier haze disturbs the visibility. However, we
note that the guidance of the depth information for transmission estimation
could remedy the decreased visibility as distances increase. In turn, the good
transmission estimation could facilitate the depth estimation for hazy images.
In this paper, a deep end-to-end model that iteratively estimates image depths
and transmission maps is proposed to perform an effective depth prediction for
hazy images and improve the dehazing performance with the guidance of depth
information. The image depth and transmission map are progressively refined to
better restore the dehazed image. Our approach benefits from explicitly
modeling the inner relationship of image depth and transmission map, which is
especially effective for distant hazy areas. Extensive results on the
benchmarks demonstrate that our proposed network performs favorably against the
state-of-the-art dehazing methods in terms of depth estimation and haze
removal.
