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Removal of rain streaks from a single image is an extremely challengingproblem since the rainy images often contain rain streaks of different size,shape, direction and density. Most recent methods for deraining use a deepnetwork following a generic "encoder-decoder" architecture which captureslow-level features across the initial layers and high-level features in thedeeper layers. For the task of deraining, the rain streaks which are to beremoved are relatively small and focusing much on global features is not anefficient way to solve the problem. To this end, we propose using anovercomplete convolutional network architecture which gives special attentionin learning local structures by restraining the receptive field of filters. Wecombine it with U-Net so that it does not lose out on the global structures aswell while focusing more on low-level features, to compute the derained image.The proposed network called, Over-and-Under Complete Deraining Network (OUCD),consists of two branches: overcomplete branch which is confined to smallreceptive field size in order to focus on the local structures and anundercomplete branch that has larger receptive fields to primarily focus onglobal structures. Extensive experiments on synthetic and real datasetsdemonstrate that the proposed method achieves significant improvements over therecent state-of-the-art methods.

Exploring Overcomplete Representations for Single Image Deraining Using CNNs