  Image-to-image translation based on generative adversarial network (GAN) has
achieved state-of-the-art performance in various image restoration
applications. Single image dehazing is a typical example, which aims to obtain
the haze-free image of a haze one. This paper concentrates on the challenging
task of single image dehazing. Based on the atmospheric scattering model, we
design a novel model to directly generate the haze-free image. The main
challenge of image dehazing is that the atmospheric scattering model has two
parameters, i.e., transmission map and atmospheric light. When we estimate them
respectively, the errors will be accumulated to compromise dehazing quality.
Considering this reason and various image sizes, we propose a novel input-size
flexibility conditional generative adversarial network (cGAN) for single image
dehazing, which is input-size flexibility at both training and test stages for
image-to-image translation with cGAN framework. We propose a simple and
effective U-type residual network (UR-Net) to combine the generator and adopt
the spatial pyramid pooling (SPP) to design the discriminator. Moreover, the
model is trained with multi-loss function, in which the consistency loss is a
novel designed loss in this paper. We finally build a multi-scale cGAN fusion
model to realize state-of-the-art single image dehazing performance. The
proposed models receive a haze image as input and directly output a haze-free
one. Experimental results demonstrate the effectiveness and efficiency of the
proposed models.
