1 research outputs found
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing
We propose an enhanced multi-scale network, dubbed GridDehazeNet+, for single
image dehazing. The proposed dehazing method does not rely on the Atmosphere
Scattering Model (ASM), and an explanation as to why it is not necessarily
performing the dimension reduction offered by this model is provided.
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 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. Due
to domain shift, the model trained on synthetic data may not generalize well on
real data. To address this issue, we shape the distribution of synthetic data
to match that of real data, and use the resulting translated data to finetune
our network. We also propose a novel intra-task knowledge transfer mechanism
that can memorize and take advantage of synthetic domain knowledge to assist
the learning process on the translated data. Experimental results demonstrate
that the proposed method outperforms the state-of-the-art on several synthetic
dehazing datasets, and achieves the superior performance on real-world hazy
images after finetuning.Comment: arXiv admin note: text overlap with arXiv:1908.0324