DCNet: Dual-task cycle network for end-to-end image dehazing

Zhihua Chen, Yu Zhou, Ping Li, Xiaoyu Chi, Lei Ma, Bin Sheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Single image dehazing is an important technology in the field of computer vision. In this paper, we propose an image dehazing via dual learning strategy, named dual-task cycle network (DCNet). The core of DCNet is a dual learning framework, which consists of two tasks: the dehazing task and the haze generation task. The dehazing task completes the image dehazing, while the haze generation task achieves the restoration from the dehazed image to the haze image and can form a cycle to provide additional supervision. Our method uses the duality between each task as a constraint to learn and train two tasks jointly, so that the effects of the dehazing model can be improved. Since the haze generation process does not depend on clear images, the DCNet can satisfy the requirements for limited supervision. Extensive experiments demonstrate that our DCNet performs favorably on haze removal.

Original languageEnglish
Title of host publication2021 IEEE International Conference on Multimedia and Expo, ICME 2021
PublisherIEEE Computer Society
Pages1-6
Number of pages6
ISBN (Electronic)9781665438643
DOIs
Publication statusPublished - Jul 2021
Event2021 IEEE International Conference on Multimedia and Expo, ICME 2021 - Shenzhen, China
Duration: 5 Jul 20219 Jul 2021

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2021 IEEE International Conference on Multimedia and Expo, ICME 2021
Country/TerritoryChina
CityShenzhen
Period5/07/219/07/21

Keywords

  • convolutional neural network
  • dual learning strategy
  • Image dehazing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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