  We propose an end-to-end trainable Convolutional Neural Network (CNN), named
GridDehazeNet, for single image dehazing. The GridDehazeNet consists of three
modules: pre-processing, backbone, and post-processing. The trainable
pre-processing module can generate learned inputs with better diversity and
more pertinent features as compared to those derived inputs produced by
hand-selected pre-processing methods. The backbone module implements a novel
attention-based multi-scale estimation on a grid network, which can effectively
alleviate the bottleneck issue often encountered in the conventional
multi-scale approach. The post-processing module helps to reduce the artifacts
in the final output. Experimental results indicate that the GridDehazeNet
outperforms the state-of-the-arts on both synthetic and real-world images. The
proposed hazing method does not rely on the atmosphere scattering model, and we
provide an explanation as to why it is not necessarily beneficial to take
advantage of the dimension reduction offered by the atmosphere scattering model
for image dehazing, even if only the dehazing results on synthetic images are
concerned.
