Non-Homogeneous Dehazing Algorithm Based on Fusion of Dual Attention and Transformer
In order to solve the problems of the lack of attention to different haze regions and the unsatisfactory recovery of detailed information within the dense haze areas by existing dehazing algorithms, we propose a non-homogeneous dehazing algorithm combining convolutional neural network and Transformer.Firstly, in order to better focus on the dense haze areas, a parallel dual attention convolutional network is constructed, with different weights assigned to pixels and channels within the image.Secondly, for deep feature extraction, Transformer blocks are introduced to capture the global non-homogeneous hazy region features, enabling the comprehensive capture of long-range dependence between features and mitigating the problem of detail loss associated with conventional convolution approaches with enlarged receptive fields.Finally, a multi-feature fusion reconstruction network is designed to adaptively fuse shallow and deep features to reconstruct clear images.Through extensive experiments conducted on both public datasets and a self-built non-homogeneous haze dataset, the results indicate that the proposed algorithm outperforms other state-of-the-art algorithms in terms of visual quality and objective evaluation metrics.
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