  Single image dehazing is a challenging ill-posed problem that has drawn
significant attention in the last few years. Recently, convolutional neural
networks have achieved great success in image dehazing. However, it is still
difficult for these increasingly complex models to recover accurate details
from the hazy image. In this paper, we pay attention to the feature extraction
and utilization of the input image itself. To achieve this, we propose a
Multi-scale Topological Network (MSTN) to fully explore the features at
different scales. Meanwhile, we design a Multi-scale Feature Fusion Module
(MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection
and fusion of features at different scales, so as to achieve progressive image
dehazing. This topological network provides a large number of search paths that
enable the network to extract abundant image features as well as strong fault
tolerance and robustness. In addition, ASFM and MFFM can adaptively select
important features and ignore interference information when fusing different
scale representations. Extensive experiments are conducted to demonstrate the
superiority of our method compared with state-of-the-art methods.
