  The presence of non-homogeneous haze can cause scene blurring, color
distortion, low contrast, and other degradations that obscure texture details.
Existing homogeneous dehazing methods struggle to handle the non-uniform
distribution of haze in a robust manner. The crucial challenge of
non-homogeneous dehazing is to effectively extract the non-uniform distribution
features and reconstruct the details of hazy areas with high quality. In this
paper, we propose a novel self-paced semi-curricular attention network, called
SCANet, for non-homogeneous image dehazing that focuses on enhancing
haze-occluded regions. Our approach consists of an attention generator network
and a scene reconstruction network. We use the luminance differences of images
to restrict the attention map and introduce a self-paced semi-curricular
learning strategy to reduce learning ambiguity in the early stages of training.
Extensive quantitative and qualitative experiments demonstrate that our SCANet
outperforms many state-of-the-art methods. The code is publicly available at
https://github.com/gy65896/SCANet.
