Learning Dual Convolutional Neural Networks for Low-Level Vision

Jinshan Pan, Sifei Liu, Deqing Sun, Jiawei Zhang, Yang Liu, Jimmy Ren, Zechao Li, Jinhui Tang, Huchuan Lu, Yu Wing Tai, Ming Hsuan Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PublisherIEEE Computer Society
Pages3070-3079
Number of pages10
ISBN (Electronic)9781538664209
DOIs
Publication statusPublished - 2018 Dec 14
Event31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States
Duration: 2018 Jun 182018 Jun 22

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
CountryUnited States
CitySalt Lake City
Period18/6/1818/6/22

Fingerprint

Neural networks

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Cite this

Pan, J., Liu, S., Sun, D., Zhang, J., Liu, Y., Ren, J., ... Yang, M. H. (2018). Learning Dual Convolutional Neural Networks for Low-Level Vision. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 (pp. 3070-3079). [8578422] (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). IEEE Computer Society. https://doi.org/10.1109/CVPR.2018.00324
Pan, Jinshan ; Liu, Sifei ; Sun, Deqing ; Zhang, Jiawei ; Liu, Yang ; Ren, Jimmy ; Li, Zechao ; Tang, Jinhui ; Lu, Huchuan ; Tai, Yu Wing ; Yang, Ming Hsuan. / Learning Dual Convolutional Neural Networks for Low-Level Vision. Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, 2018. pp. 3070-3079 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
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abstract = "In this paper, we propose a general dual convolutional neural network (DualCNN) for low-level vision problems, e.g., super-resolution, edge-preserving filtering, deraining and dehazing. These problems usually involve the estimation of two components of the target signals: structures and details. Motivated by this, our proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner. The recovered structures and details can generate the target signals according to the formation model for each particular application. The DualCNN is a flexible framework for low-level vision tasks and can be easily incorporated into existing CNNs. Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods.",
author = "Jinshan Pan and Sifei Liu and Deqing Sun and Jiawei Zhang and Yang Liu and Jimmy Ren and Zechao Li and Jinhui Tang and Huchuan Lu and Tai, {Yu Wing} and Yang, {Ming Hsuan}",
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Pan, J, Liu, S, Sun, D, Zhang, J, Liu, Y, Ren, J, Li, Z, Tang, J, Lu, H, Tai, YW & Yang, MH 2018, Learning Dual Convolutional Neural Networks for Low-Level Vision. in Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018., 8578422, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, pp. 3070-3079, 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, United States, 18/6/18. https://doi.org/10.1109/CVPR.2018.00324

Learning Dual Convolutional Neural Networks for Low-Level Vision. / Pan, Jinshan; Liu, Sifei; Sun, Deqing; Zhang, Jiawei; Liu, Yang; Ren, Jimmy; Li, Zechao; Tang, Jinhui; Lu, Huchuan; Tai, Yu Wing; Yang, Ming Hsuan.

Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, 2018. p. 3070-3079 8578422 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Pan J, Liu S, Sun D, Zhang J, Liu Y, Ren J et al. Learning Dual Convolutional Neural Networks for Low-Level Vision. In Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society. 2018. p. 3070-3079. 8578422. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2018.00324