  Hazy images reduce the visibility of the image content, and haze will lead to
failure in handling subsequent computer vision tasks. In this paper, we address
the problem of image dehazing by proposing a dehazing network named T-Net,
which consists of a backbone network based on the U-Net architecture and a dual
attention module. And it can achieve multi-scale feature fusion by using skip
connections with a new fusion strategy. Furthermore, by repeatedly unfolding
the plain T-Net, Stack T-Net is proposed to take advantage of the dependence of
deep features across stages via a recursive strategy. In order to reduce
network parameters, the intra-stage recursive computation of ResNet is adopted
in our Stack T-Net. And we take both the stage-wise result and the original
hazy image as input to each T-Net and finally output the prediction of clean
image. Experimental results on both synthetic and real-world images demonstrate
that our plain T-Net and the advanced Stack T-Net perform favorably against the
state-of-the-art dehazing algorithms, and show that our Stack T-Net could
further improve the dehazing effect, demonstrating the effectiveness of the
recursive strategy.

Computer Vision and Pattern Recognition