Double Recurrent Dense Network for Single Image Deraining

Rain streaks can affect visual visibility, and hence disable many visual algorithms. So we present a double recurrent dense network for removing rain streaks from single image. Assume the rain image is the superposition of the clean image and the rain streaks, we directly learn the rain streaks from...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.30615-30627
Main Authors: Lan, Yang, Xia, Haiying, Li, Haisheng, Song, Shuxiang, Wu, Lingyu
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
dense network
Deraining
double
Image contrast
Image denoising
Iterative methods
Noise reduction
Predictive models
Rain
recurrent neural network
Task analysis
Visibility
Visualization
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Summary:Rain streaks can affect visual visibility, and hence disable many visual algorithms. So we present a double recurrent dense network for removing rain streaks from single image. Assume the rain image is the superposition of the clean image and the rain streaks, we directly learn the rain streaks from the rainy image. In contrast to other models, we introduce a double recurrent scheme to promote better information reuse of rain streaks and relative clean image. For rain streaks, the LSTM cascaded by DenseNet blocks is used as the basic model. The relative clean image predicted by subtracting the rain streaks from the rainy image is then input to the basic model in an iterative way. Benefiting from double recurrent schemes, our model makes full use of rain streaks and image detail information and thoroughly removes rain streaks. Furthermore, we adopt a mix of L_{1} loss, L_{2} loss and SSIM loss to guarantee good rain removing performance. We conduct a plenty of experiments on synthetic and real rainy images, even on similar denoise task, the results not only show our model significantly outperforms the state-of-art methods for removing rain streaks, but also exhibit our model has a high effectiveness for similar task, i.e. image denoising.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.2972909