Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM

• URL: http://arxiv.org/abs/2111.03615v1
• Date: Fri, 5 Nov 2021 17:03:08 GMT
• Title: Single Image Deraining Network with Rain Embedding Consistency and Layered LSTM
• Authors: Yizhou Li and Yusuke Monno and Masatoshi Okutomi
• Abstract summary: We introduce the idea of "Rain Embedding Consistency" by regarding the encoded embedding by the autoencoder as an ideal rain embedding. A Rain Embedding Loss is applied to directly supervise the encoding process, with a Rectified Local Contrast Normalization as the guide. We also propose Layered LSTM for recurrent deraining and fine-grained encoder feature refinement considering different scales.
• Score: 14.310541943673181
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single image deraining is typically addressed as residual learning to predict the rain layer from an input rainy image. For this purpose, an encoder-decoder network draws wide attention, where the encoder is required to encode a high-quality rain embedding which determines the performance of the subsequent decoding stage to reconstruct the rain layer. However, most of existing studies ignore the significance of rain embedding quality, thus leading to limited performance with over/under-deraining. In this paper, with our observation of the high rain layer reconstruction performance by an rain-to-rain autoencoder, we introduce the idea of "Rain Embedding Consistency" by regarding the encoded embedding by the autoencoder as an ideal rain embedding and aim at enhancing the deraining performance by improving the consistency between the ideal rain embedding and the rain embedding derived by the encoder of the deraining network. To achieve this, a Rain Embedding Loss is applied to directly supervise the encoding process, with a Rectified Local Contrast Normalization (RLCN) as the guide that effectively extracts the candidate rain pixels. We also propose Layered LSTM for recurrent deraining and fine-grained encoder feature refinement considering different scales. Qualitative and quantitative experiments demonstrate that our proposed method outperforms previous state-of-the-art methods particularly on a real-world dataset. Our source code is available at http://www.ok.sc.e.titech.ac.jp/res/SIR/.

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