TSAN et: Two-subnet Attention Network for Single Image Dehazing
Chenran Jiang, Fei Yan, Tao Deng, Lin Jun Sun, Jun Li · 2022
Haze restricts visual quality and degrades the qual-ity of captured images. The aim of single image dehazing is to recover a haze-free image from a hazy one. However, most present image dehazing methods treat different feature information in channels and pixels evenly, which may influence the dehazing result because of the uneven distribution of haze. To address hazy uneven distribution, we propose an end-to-end two-sub net attention network (TSANet), which consists of attention-recurrent (AR) and asymmetric u-shaped dehazing refinement (AUDR). In addition, a feature residual attention (FRA) block is designed to focus on thick-hazy regions and high-frequency regions of a hazy image when dehazing. In the model, the input image is first fed into the AR sub-network to extract feature information like thick hazy regions and high-frequency regions. For further feature refinement, we propose the AUDR sub-network to further process feature information from the AR sub-network. The AUDR sub-network adopts an encoder-decoder module containing FRA and transformer blocks to further process feature information of high-frequency regions and filter hazy feature information, and uses skip connections to enhance the representation of our TSANet. The extensive experimental results demonstrate the effectiveness of our method and outperform other dehazing methods on synthetic and real-world hazy datasets.