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Single-image deraining via a Recurrent Memory Unit Network
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DOI：10.1016/j.knosys.2021.106832.png)
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Abstract
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Single-image rain removal is a concern in the field of computer vision because rain streaks may reduce image quality. Images captured in rainy days may suffer from non-uniform rain consisting of different densities, shapes, and sizes. In this paper, we propose a novel single-image deraining method called a Recurrent Memory Unit Network (RMUN) to remove rain streaks from individual images. Unlike existing methods, the RMUN is a recurrent network, which can efficiently utilize the results of the current cycle for the next cycle. In addition, the RMUN employs a Residual Memory Unit Block (RMUB) to extract the features, which means that more attention can be paid to the channels of feature map. A Memory Unit block (MUB) is put in the transform path of the network to keep track of rain details. Different levels of features can be passed in the skip connections between the RMUB and MUB. The extensive experiments show that our proposed method performs better than the state-of-the-art methods on synthetic and real-world datasets. (C) 2021 Elsevier B.V. All rights reserved.
Keywords:
Deraining
Recurrent neural network
Channel attention
Aided driving
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