Existing dehazing methods based on convolutional neural networks estimate the transmission map by treating channel-wise features equally, which lacks flexibility in handling different types of haze information, leading to the poor representational ability of the network. Besides, the scene lights are predicted by an even illumination prior which does not work for a real situation. To solve these problems, the authors propose a dense residual channel attention network (DRCAN) for estimating the transmission map and use an image segmentation strategy to predict scene lights. Specifically, DRCAN is built based on the proposed dense residual block (DRB) and dense residual channel attention block (DRCAB). DRB extracts the hierarchical features with increasing receptive fields. DRCAB makes the network focus on the features containing heavy haze information. After the transmission map is estimated, fuzzy partition entropy combined with graph cuts is used to segment the transmission map into scene regions covered with varying scene lights. This strategy not only considers the fuzzy intensities of the low-contrast transmission map but also takes spatial correlation into account. Finally, a clear image is obtained by the transmission map and varying scene lights. Extensive experiments demonstrate that our method is comparable to most of existing methods.

中文翻译：

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密集残差通道注意网络对光照不均匀的图像进行去雾

现有的基于卷积神经网络的除雾方法通过均等地处理信道特征来估计传输图，这在处理不同类型的雾度信息时缺乏灵活性，导致网络的表示能力较差。此外，场景光是通过均匀照明来预测的，而在实际情况下该照明是无效的。为了解决这些问题，作者提出了一个密集的剩余信道关注网络（DRCAN）来估计传输图，并使用图像分割策略来预测场景光。具体来说，DRCAN是基于提议的密集残差块（DRB）和密集残差信道注意块（DRCAB）构建的。DRB提取具有增加的接收场的层次特征。DRCAB使网络专注于包含大量雾霾信息的功能。在估计了透射图之后，使用模糊分割熵结合图割来将透射图分割成覆盖有变化场景光的场景区域。该策略不仅考虑了低对比度透射图的模糊强度，而且还考虑了空间相关性。最后，通过透射图和变化的场景光获得清晰的图像。大量实验表明，我们的方法可与大多数现有方法媲美。该策略不仅考虑了低对比度透射图的模糊强度，而且还考虑了空间相关性。最后，通过透射图和变化的场景光获得清晰的图像。大量实验表明，我们的方法可与大多数现有方法媲美。该策略不仅考虑了低对比度透射图的模糊强度，而且还考虑了空间相关性。最后，通过透射图和变化的场景光获得清晰的图像。大量实验表明，我们的方法可与大多数现有方法媲美。