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Different rain models and novel network structures have been proposed toremove rain streaks from single rainy images. In this work, we bring attentionto the intrinsic priors and multi-scale features of the rainy images, anddevelop several intrinsic loss functions to train a CNN deraining network. Wefirst study the sparse priors of rainy images, which have been verified topreserve unbroken edges in image decomposition. However, its mathematicalformulation usually leads to an intractable solution, we propose quasi-sparsitypriors to decrease complexity, so that our network can be trained under thesupervision of sparse properties of rainy images. Quasi-sparsity supervisesnetwork training in different gradient domain which is still ill-posed todecompose a rainy image into rain layer and background layer. We developanother $L_1$ loss based on the intrinsic low-value property of rain layer torestore image contents together with the commonly-used $L_1$ similarity loss.Multi-scale features are further explored via a multi-scale auxiliary decodingstructure to show which kinds of features contribute the most to the derainingtask, and the corresponding multi-scale auxiliary loss improves the derainingperformance further. In our network, more efficient group convolution andfeature sharing are utilized to obtain an one order of magnitude improvement innetwork running speed. The proposed deraining method performs favorably againststate-of-the-art deraining approaches.

Deep Image Deraining Via Intrinsic Rainy Image Priors and Multi-scale Auxiliary Decoding