Aiming at the problem that the traditional prior information-based defogging algorithm fails in some special scenarios, an end-to-end convolutional defogging network based on attention mechanism is proposed. The network consists of two modules: parameter estimation and image restoration. First, multi-scale convolution is used to extract image feature information. Residual network and skip connection methods are used to improve the utilisation rate of shallow network feature information. Secondly, the channel domain attention is used to add weight to the feature image input from the previous network and select useful feature information. Finally, the atmospheric visibility model is combined to achieve image visibility restoration. The experimental results show that the proposed algorithm can effectively improve the visibility of the image and the restoration effect is natural. The objective evaluation index of the benchmark datasets also shows the effectiveness of the algorithm.

中文翻译：

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基于注意力的端到端图像去雾网络

针对传统的基于先验信息的去雾算法在一些特殊场景下失效的问题，提出了一种基于注意力机制的端到端卷积去雾网络。该网络由两个模块组成：参数估计和图像恢复。首先，使用多尺度卷积提取图像特征信息。使用残差网络和跳跃连接方法提高浅层网络特征信息的利用率。其次，通道域注意力用于对先前网络输入的特征图像增加权重，选择有用的特征信息。最后结合大气能见度模型实现图像能见度恢复。实验结果表明，所提算法能有效提高图像的可见度，还原效果自然。基准数据集的客观评价指标也说明了算法的有效性。