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

doi:10.1109/CVPR.2018.00324

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

60 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 language	English
Title of host publication	Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Publisher	IEEE Computer Society
Pages	3070-3079
Number of pages	10
ISBN (Electronic)	9781538664209
DOIs	https://doi.org/10.1109/CVPR.2018.00324
Publication status	Published - 2018 Dec 14
Event	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States Duration: 2018 Jun 18 → 2018 Jun 22

Publication series

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

Conference

Conference	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Country	United States
City	Salt Lake City
Period	18/6/18 → 18/6/22

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2018.00324

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Pan, J., Liu, S., Sun, D., Zhang, J., Liu, Y., Ren, J., Li, Z., Tang, J., Lu, H., Tai, Y. W., & 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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title = "Learning Dual Convolutional Neural Networks for Low-Level Vision",

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 proceeding › Conference contribution

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AU - Sun, Deqing

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AU - Liu, Yang

AU - Ren, Jimmy

AU - Li, Zechao

AU - Tang, Jinhui

AU - Lu, Huchuan

AU - Tai, Yu Wing

AU - Yang, Ming Hsuan

PY - 2018/12/14

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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