Single image rain streak removal via layer similarity prior


Single image rain streak removal is a significant and challenging task, which is widely applied in many artificial intelligence domains as preprocessing process. Most of existing rain streak removal works focus on designing various deraining unit (e.g., multi-stream dilation convolution) without considering the correlation between different convolution layers, which may lead to large model size. In this paper, we propose a simple and effective deep network architecture for single image rain streak removal based on deep Convolutional Neural Network (CNN). Benefit from the adjacent layers with different dilation factors have similar feature structures, we design a powerful rain streak representation network based on the Layer Similarity Prior Block (LSPB). To better cater to the property of layer similarity prior, the multi-dense-short-connection is developed and it regards every LSPB as a convolution layer, this connection style makes our overall framework to be a layer similarity prior network. To the best of our knowledge, this is the first paper to investigate the effectiveness of exploiting the layer similarity prior and the multi-dense-short-connection. Quantitative and qualitative experimental results demonstrate that the proposed method outperforms other state-of-the-art methods with the least parameters.

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This work was supported by the National Natural Science Foundation of China [grant numbers 61976041]; Major National Science and Technology Project of China [grant number 2018ZX04041001].

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Correspondence to Wanshu Fan.

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Fan, W., Wu, Y. & Wang, C. Single image rain streak removal via layer similarity prior. Appl Intell (2021).

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  • Deraining
  • Deep-learning
  • Layer similarity prior
  • Multi-dense-short-connection