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 densityaware 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 raindensity 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. The code can be downloaded at https://github.com/hezhangsprinter/DID-MDN
In REF , Zhang et al. propose a density-aware multi-stream densely connected convolutional neural network (DID-MDN) for joint rain density estimation and rain removal.
Density-Aware Single Image De-raining Using a Multi-stream Dense Network
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Computer Science