  In recent years, single image dehazing models (SIDM) based on atmospheric
scattering model (ASM) have achieved remarkable results. However, it is noted
that ASM-based SIDM degrades its performance in dehazing real world hazy images
due to the limited modelling ability of ASM where the atmospheric light factor
(ALF) and the angular scattering coefficient (ASC) are assumed as constants for
one image. Obviously, the hazy images taken in real world cannot always satisfy
this assumption. Such generating modelling mismatch between the real-world
images and ASM sets up the upper bound of trained ASM-based SIDM for dehazing.
Bearing this in mind, in this study, a new fully non-homogeneous atmospheric
scattering model (FNH-ASM) is proposed for well modeling the hazy images under
complex conditions where ALF and ASC are pixel dependent. However, FNH-ASM
brings difficulty in practical application. In FNH-ASM based SIDM, the
estimation bias of parameters at different positions lead to different
distortion of dehazing result. Hence, in order to reduce the influence of
parameter estimation bias on dehazing results, two new cost sensitive loss
functions, beta-Loss and D-Loss, are innovatively developed for limiting the
parameter bias of sensitive positions that have a greater impact on the
dehazing result. In the end, based on FNH-ASM, an end-to-end CNN-based dehazing
network, FNHD-Net, is developed, which applies beta-Loss and D-Loss.
Experimental results demonstrate the effectiveness and superiority of our
proposed FNHD-Net for dehazing on both synthetic and real-world images. And the
performance improvement of our method increases more obviously in dense and
heterogeneous haze scenes.
