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

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

226 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
Pages	3070-3079
Number of pages	10
ISBN (Electronic)	9781538664209
DOIs	https://doi.org/10.1109/CVPR.2018.00324
Publication status	Published - 14 Dec 2018
Externally published	Yes
Event	31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 - Salt Lake City, United States Duration: 18 Jun 2018 → 22 Jun 2018

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/Territory	United States
City	Salt Lake City
Period	18/06/18 → 22/06/18

Access to Document

10.1109/CVPR.2018.00324

Cite this

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). Article 8578422 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition). https://doi.org/10.1109/CVPR.2018.00324

@inproceedings{267002465bf14ebbba63bade5f9b8f70,

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\}",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 ; Conference date: 18-06-2018 Through 22-06-2018",

year = "2018",

month = dec,

day = "14",

doi = "10.1109/CVPR.2018.00324",

language = "English",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

pages = "3070--3079",

booktitle = "Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018",

}

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, pp. 3070-3079, 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, United States, 18/06/18. https://doi.org/10.1109/CVPR.2018.00324

Learning Dual Convolutional Neural Networks for Low-Level Vision. / Pan, Jinshan; Liu, Sifei; Sun, Deqing et al.
Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. 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 › peer-review

TY - GEN

T1 - Learning Dual Convolutional Neural Networks for Low-Level Vision

AU - Pan, Jinshan

AU - Liu, Sifei

AU - Sun, Deqing

AU - Zhang, Jiawei

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

Y1 - 2018/12/14

N2 - 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.

AB - 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.

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M3 - Conference contribution

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BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018

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ER -

Learning Dual Convolutional Neural Networks for Low-Level Vision

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