  Aiming at the existing single image haze removal algorithms, which are based
on prior knowledge and assumptions, subject to many limitations in practical
applications, and could suffer from noise and halo amplification. An end-to-end
system is proposed in this paper to reduce defects by combining the prior
knowledge and deep learning method. The haze image is decomposed into the base
layer and detail layers through a weighted guided image filter (WGIF) firstly,
and the airlight is estimated from the base layer. Then, the base layer image
is passed to the efficient deep convolutional network for estimating the
transmission map. To restore object close to the camera completely without
amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed
based on the value of the transmission map. If the transmission map of a pixel
is small, the base layer of the haze image is used to recover a haze-free image
via atmospheric scattering model, finally. Otherwise, the haze image is used.
Experiments show that the proposed method achieves superior performance over
existing methods.
