  We propose an enhanced multi-scale network, dubbed GridDehazeNet+, for single
image dehazing. It 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 multi-scale estimation with two major enhancements: 1) a
novel grid structure that effectively alleviates the bottleneck issue via dense
connections across different scales; 2) a spatial-channel attention block that
can facilitate adaptive fusion by consolidating dehazing-relevant features. The
post-processing module helps to reduce the artifacts in the final output. To
alleviate domain shift between network training and testing, we convert
synthetic data to so-called translated data with the distribution shaped to
match that of real data. Moreover, to further improve the dehazing performance
in real-world scenarios, we propose a novel intra-task knowledge transfer
mechanism that leverages the distilled knowledge from synthetic data to assist
the learning process on translated data. Experimental results indicate that the
proposed GridDehazeNet+ outperforms the state-of-the-art methods on several
dehazing benchmarks. The proposed dehazing method does not rely on the
atmosphere scattering model, and we provide a possible explanation as to why it
is not necessarily beneficial to take advantage of the dimension reduction
offered by this model, even if only the dehazing results on synthetic images
are concerned.
