Structural residual learning for single image rain removal
To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of these deep learning methods largely relies on the covering range of rain shapes contained in the pre-collected training rain...
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| Vydané v: | Knowledge-based systems Ročník 213; s. 106595 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Amsterdam
Elsevier B.V
15.02.2021
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of these deep learning methods largely relies on the covering range of rain shapes contained in the pre-collected training rainy-clean image pairs. This makes them easily trapped into the overfitting-to-the-training-samples issue and cannot finely generalize to practical rainy images with complex and diverse rain streaks. Against this generalization issue, this study proposes a new network architecture by enforcing the output residual of the network possess intrinsic rain structures. Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages. Such a general regularization function naturally leads to both its better training accuracy and testing generalization capability even for those non-seen rain configurations. Such superiority is comprehensively substantiated by experiments implemented on synthetic and real datasets both visually and quantitatively as compared with current state-of-the-art methods. |
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| AbstractList | To alleviate the adverse effect of rain streaks in image processing tasks, CNN-based single image rain removal methods have been recently proposed. However, the performance of these deep learning methods largely relies on the covering range of rain shapes contained in the pre-collected training rainy-clean image pairs. This makes them easily trapped into the overfitting-to-the-training-samples issue and cannot finely generalize to practical rainy images with complex and diverse rain streaks. Against this generalization issue, this study proposes a new network architecture by enforcing the output residual of the network possess intrinsic rain structures. Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages. Such a general regularization function naturally leads to both its better training accuracy and testing generalization capability even for those non-seen rain configurations. Such superiority is comprehensively substantiated by experiments implemented on synthetic and real datasets both visually and quantitatively as compared with current state-of-the-art methods. |
| ArticleNumber | 106595 |
| Author | Meng, Deyu Xie, Qi Wang, Hong Zhao, Qian Liang, Yong Wu, Yichen Zhang, Shijun |
| Author_xml | – sequence: 1 givenname: Hong surname: Wang fullname: Wang, Hong organization: Xi’an Jiaotong University, Shaanxi, 710049, PR China – sequence: 2 givenname: Yichen surname: Wu fullname: Wu, Yichen organization: Xi’an Jiaotong University, Shaanxi, 710049, PR China – sequence: 3 givenname: Qi surname: Xie fullname: Xie, Qi email: xq.liwu@stu.xjtu.edu.cn organization: Xi’an Jiaotong University, Shaanxi, 710049, PR China – sequence: 4 givenname: Qian orcidid: 0000-0001-9956-0064 surname: Zhao fullname: Zhao, Qian organization: Xi’an Jiaotong University, Shaanxi, 710049, PR China – sequence: 5 givenname: Yong surname: Liang fullname: Liang, Yong email: yliang@must.edu.mo organization: Macau University of Science and Technology, Macau, PR China – sequence: 6 givenname: Shijun surname: Zhang fullname: Zhang, Shijun organization: China Mobile Research Institute, Beijing, PR China – sequence: 7 givenname: Deyu surname: Meng fullname: Meng, Deyu organization: Xi’an Jiaotong University, Shaanxi, 710049, PR China |
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| Keywords | Deep learning Generalization performance Single image deraining Interpretability Multi-scale convolutional sparse coding |
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| SubjectTerms | Computer architecture Deep learning Experiments Generalization Generalization performance Image processing Interpretability Learning Multi-scale convolutional sparse coding Networks Prior knowledge Rain Regularization Side effects Single image deraining Training |
| Title | Structural residual learning for single image rain removal |
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