Abstract
Recently, deep learning-based single image dehazing method has been a popular approach to tackle dehazing. However, the existing dehazing approaches are performed directly on the original
hazy image, which easily results in image blurring
and noise amplifying. To address this issue, the paper proposes a DPDP-Net (Dual-Path in Dual-Path
network) framework by employing a hierarchical dual path network. Specifically, the first-level dualpath network consists of a Dehazing Network and a
Denoising Network, where the Dehazing Network
is responsible for haze removal in the structural layer, and the Denoising Network deals with noise in
the textural layer, respectively. And the secondlevel dual-path network lies in the Dehazing Network, which has an AL-Net (Atmospheric Light
Network) and a TM-Net (Transmission Map Network), respectively. Concretely, the AL-Net aims
to train the non-uniform atmospheric light, while
the TM-Net aims to train the transmission map that
reflects the visibility of the image. The final dehazing image is obtained by nonlinearly fusing the
output of the Denoising Network and the Dehazing Network. Extensive experiments demonstrate
that our proposed DPDP-Net achieves competitive
performance against the state-of-the-art methods on
both synthetic and real-world images