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 |
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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 |
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ISSN (Print) | 1063-6919 |
Conference
Conference | 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018 |
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Country/Territory | United States |
City | Salt Lake City |
Period | 18/6/18 → 18/6/22 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition