Where can distinguishing features be extracted in an image for
Standard convolution is difficult to provide an effective fog feature
for visibility estimate tasks due to the fixed grid kernel structure. In
this paper, a multiscale deformable convolution model (MDCM) is proposed
to extract features that make effectively sampling discriminating
features from the atmospheric region in foggy image. Moreover, to
enhance performance we use RGB-IR image pair as observations and design
a multimodal visibility range classification network based on the MDCM.
Experimental results show that both the robustness and accuracy of
visibility estimate performance are raised beyond 30% compared to
standard convolutional neural networks (CNNs).