Scale-Free Single Image Deraining Via Visibility-Enhanced Recurrent Wavelet Learning

In this paper, we address a rain removal problem from a single image, even in the presence of large rain streaks and rain streak accumulation (where individual streaks cannot be seen and thus are visually similar to mist or fog). For rain streak removal, the mismatch problem between different streak...

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Vydáno v:IEEE transactions on image processing Ročník 28; číslo 6; s. 2948 - 2961
Hlavní autoři: Yang, Wenhan, Liu, Jiaying, Yang, Shuai, Guo, Zongming
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Accumulation
Adaptation models
Degradation
Image enhancement
Rain
Recovery
recurrent process
Redundancy
residual dense network
scale-free
Single image deraining
Testing
Training
Visibility
wavelet transform
Wavelet transforms
ISSN:1057-7149, 1941-0042, 1941-0042
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Popis
Shrnutí:In this paper, we address a rain removal problem from a single image, even in the presence of large rain streaks and rain streak accumulation (where individual streaks cannot be seen and thus are visually similar to mist or fog). For rain streak removal, the mismatch problem between different streak sizes in training and testing phases leads to poor performance, especially when there are large streaks. To mitigate this problem, we embed a hierarchical representation of wavelet transform into a recurrent rain removal process: 1) rain removal on the low-frequency component and 2) recurrent detail recovery on high-frequency components under the guidance of the recovered low-frequency component. Benefiting from the recurrent multi-scale modeling of wavelet transform-like design, the proposed network trained on streaks with one size can adapt to those with larger sizes, which significantly favors real rain streak removal. The dilated residual dense network is used as the basic model of the recurrent recovery process. The network includes multiple paths with different receptive fields, thus it can make full use of multi-scale redundancy and utilize context information in large regions. Furthermore, to handle heavy rain cases where rain streak accumulation is presented, we construct a detail appearing rain accumulation removal to not only improve the visibility but also enhance the details in dark regions. The evaluation of both synthetic and real images, particularly on those containing large rain streaks and heavy accumulation, shows the effectiveness of our novel models, which significantly outperforms the state-of-the-art methods.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2019.2892685