  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: https://github.com/hezhangsprinter
