  Image dehazing is an active topic in low-level vision, and many image
dehazing networks have been proposed with the rapid development of deep
learning. Although these networks' pipelines work fine, the key mechanism to
improving image dehazing performance remains unclear. For this reason, we do
not target to propose a dehazing network with fancy modules; rather, we make
minimal modifications to popular U-Net to obtain a compact dehazing network.
Specifically, we swap out the convolutional blocks in U-Net for residual blocks
with the gating mechanism, fuse the feature maps of main paths and skip
connections using the selective kernel, and call the resulting U-Net variant
gUNet. As a result, with a significantly reduced overhead, gUNet is superior to
state-of-the-art methods on multiple image dehazing datasets. Finally, we
verify these key designs to the performance gain of image dehazing networks
through extensive ablation studies.
