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 | |
| 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 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition
Cite this
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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
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.
UR - http://www.scopus.com/inward/record.url?scp=85053748887&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053748887&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2018.00324
DO - 10.1109/CVPR.2018.00324
M3 - Conference contribution
AN - SCOPUS:85053748887
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 3070
EP - 3079
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
PB - IEEE Computer Society
ER -