Abstract: In recent years, single image dehazing deep models based on Atmospheric
Scattering Model (ASM) have achieved remarkable results. But the dehazing
outputs of those models suffer from color shift. Analyzing the ASM model shows
that the atmospheric light factor (ALF) is set as a scalar which indicates ALF
is constant for whole image. However, for images taken in real-world, the
illumination is not uniformly distributed over whole image which brings model
mismatch and possibly results in color shift of the deep models using ASM.
Bearing this in mind, in this study, first, a new non-homogeneous atmospheric
scattering model (NH-ASM) is proposed for improving image modeling of hazy
images taken under complex illumination conditions. Second, a new U-Net based
front white balance module (FWB-Module) is dedicatedly designed to correct
color shift before generating dehazing result via atmospheric light estimation.
Third, a new FWB loss is innovatively developed for training FWB-Module, which
imposes penalty on color shift. In the end, based on NH-ASM and front white
balance technology, an end-to-end CNN-based color-shift-restraining dehazing
network is developed, termed as FWB-Net. Experimental results demonstrate the
effectiveness and superiority of our proposed FWB-Net for dehazing on both
synthetic and real-world images.