Semi-Supervised Image Deraining Using Knowledge Distillation
Image deraining has achieved considerable progress based on supervised learning with synthetic training pairs, but is usually limited in handling real-world rainy images. Although semi-supervised methods are suggested to exploit real-world rainy images when training deep deraining models, their perf...
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| Vydané v: | IEEE transactions on circuits and systems for video technology Ročník 32; číslo 12; s. 8327 - 8341 |
|---|---|
| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
IEEE
01.12.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1051-8215, 1558-2205 |
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| Abstract | Image deraining has achieved considerable progress based on supervised learning with synthetic training pairs, but is usually limited in handling real-world rainy images. Although semi-supervised methods are suggested to exploit real-world rainy images when training deep deraining models, their performances are still notably inferior. To address this crucial issue, this work proposes a semi-supervised image deraining network with knowledge distillation (SSID-KD) for better exploiting real-world rainy images. In particular, the consistency of feature distribution of rain streaks extracted from synthetic and real-world rainy images is enforced by adopting knowledge distillation. Moreover, as for the backbone in SSID-KD, we propose the multi-scale feature fusion module and the pyramid fusion module to better extract deep features of rainy images. SSID-KD can relieve the problem of over-deraining or under-deraining for real-world rainy images, while it can keep comparable performance with supervised deraining methods on several benchmark datasets. Extensive experiments on both synthetic and real-world rainy images have validated that our SSID-KD not only can achieve better deraining results than existing semi-supervised deraining methods but also are quantitatively comparable with state-of-the-art supervised deraining methods. Benefiting from the well exploration of real-world rainy images, our SSID-KD can obtain more visually plausible deraining results. The source code and trained models are publicly available at https://github.com/cuiyixin555/SSID-KD . |
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| AbstractList | Image deraining has achieved considerable progress based on supervised learning with synthetic training pairs, but is usually limited in handling real-world rainy images. Although semi-supervised methods are suggested to exploit real-world rainy images when training deep deraining models, their performances are still notably inferior. To address this crucial issue, this work proposes a semi-supervised image deraining network with knowledge distillation (SSID-KD) for better exploiting real-world rainy images. In particular, the consistency of feature distribution of rain streaks extracted from synthetic and real-world rainy images is enforced by adopting knowledge distillation. Moreover, as for the backbone in SSID-KD, we propose the multi-scale feature fusion module and the pyramid fusion module to better extract deep features of rainy images. SSID-KD can relieve the problem of over-deraining or under-deraining for real-world rainy images, while it can keep comparable performance with supervised deraining methods on several benchmark datasets. Extensive experiments on both synthetic and real-world rainy images have validated that our SSID-KD not only can achieve better deraining results than existing semi-supervised deraining methods but also are quantitatively comparable with state-of-the-art supervised deraining methods. Benefiting from the well exploration of real-world rainy images, our SSID-KD can obtain more visually plausible deraining results. The source code and trained models are publicly available at https://github.com/cuiyixin555/SSID-KD . |
| Author | Chen, Yunjin Ren, Dongwei Zhu, Pengfei Cui, Xin Wang, Cong |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0000-0002-7088-3799 surname: Cui fullname: Cui, Xin email: 2019216101@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Tianjin, China – sequence: 2 givenname: Cong surname: Wang fullname: Wang, Cong email: supercong94@gmail.com organization: Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong – sequence: 3 givenname: Dongwei orcidid: 0000-0002-0965-6810 surname: Ren fullname: Ren, Dongwei email: rendongweihit@gmail.com organization: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China – sequence: 4 givenname: Yunjin orcidid: 0000-0002-4428-2797 surname: Chen fullname: Chen, Yunjin email: chenyunjin_nudt@hotmail.com organization: Alibaba Cloud, Hangzhou, China – sequence: 5 givenname: Pengfei orcidid: 0000-0002-4310-9140 surname: Zhu fullname: Zhu, Pengfei email: zhupengfei@tju.edu.cn organization: College of Intelligence and Computing, Tianjin University, Tianjin, China |
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| SubjectTerms | Distillation Feature extraction knowledge distillation Modules Network architecture semi-supervised learning Semisupervised learning Single image deraining Source code Supervised learning Task analysis Training |
| Title | Semi-Supervised Image Deraining Using Knowledge Distillation |
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