Abstract: Most of the existing dehazing algorithms lack of attention to different concentrations of hazy images,which will lead to unsatisfactory recovery of the details in dense haze areas. Furthermore, image dehazing is a pixel-level reconstruction process,detail extraction is critical to image restoration. To address this issue, this paper proposes a non-homogeneous dehazing algorithm based on fusion of dual attention convolution and Transformer. Firstly, in shallow feature extraction, in order to improve the attention to areas of the dense haze, a parallel dual attention convolution network is constructed to assign different weights to the images from the perspective of pixels and channels. Secondly, in deep feature extraction, a Transformer block is integrated into the global non-homogeneous hazy region features, which can fully capture the long-range dependence between features and avoid the problem of detail loss in ordinary convolution enlarged receptive fields. Finally, a multi-feature fusion reconstruction network is designed to adaptively fuse shallow and deep features to reconstruct clear images. To verify the effectiveness of the algorithm, experiments are conducted on I-HAZE, O-HAZE, NH-HAZE, self-built non-homogeneous hazy datasets, and SOTS. The experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art comparative algorithms in terms of visual effects and objective evaluation metrics.

Key words: non-homogeneous dehazing, dual attention convolution, Transformer block, multi-feature fusion reconstruction network