Learning Dual Convolutional Neural Networks for Low-Level Vision

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

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

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
PublisherIEEE
Pages3070-3079
ISBN (Print)978-1-5386-6420-9
Publication statusPublished - Jun 2018

Publication series

NameIEEE Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Title31st IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018)
PlaceUnited States
CitySalt Lake City
Period18 - 23 June 2018

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.

Research Area(s)

  • REMOVAL

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

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, 2018. p. 3070-3079 (IEEE Conference on Computer Vision and Pattern Recognition).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review