Single image rain streak removal is an extremely challenging problem due to
the presence of non-uniform rain densities in images. We present a novel
density-aware multi-stream densely connected convolutional neural network-based
algorithm, called DID-MDN, for joint rain density estimation and de-raining.
The proposed method enables the network itself to automatically determine the
rain-density information and then efficiently remove the corresponding
rain-streaks guided by the estimated rain-density label. To better characterize
rain-streaks with different scales and shapes, a multi-stream densely connected
de-raining network is proposed which efficiently leverages features from
different scales. Furthermore, a new dataset containing images with
rain-density labels is created and used to train the proposed density-aware
network. Extensive experiments on synthetic and real datasets demonstrate that
the proposed method achieves significant improvements over the recent
state-of-the-art methods. In addition, an ablation study is performed to
demonstrate the improvements obtained by different modules in the proposed
method. Code can be found at: this https URL

提出一种基于密度感知的多流密集连接卷积神经网络算法来进行降雨密度估计和去雨，通过应用于训练了降雨密度标签的新数据集，该算法能有效地消除图像中对应雨纹，并能够在多尺度与不同形态下运用不同比例的特征以更好地表征雨纹。