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.
@article{arxiv.2304.08444,
title = {SCANet: Self-Paced Semi-Curricular Attention Network for Non-Homogeneous Image Dehazing},
author = {Yu Guo and Yuan Gao and Ryan Wen Liu and Yuxu Lu and Jingxiang Qu and Shengfeng He and Wenqi Ren},
journal= {arXiv preprint arXiv:2304.08444},
year = {2023}
}