Gated Fusion Network for Single Image Dehazing

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

225 Scopus Citations
View graph of relations

Author(s)

  • Wenqi Ren
  • Lin Ma
  • Jinshan Pan
  • Xiaochun Cao
  • Wei Liu
  • 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
Pages3253-3261
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 an efficient algorithm to directly restore a clear image from a hazy input. The proposed algorithm hinges on an end-to-end trainable neural network that consists of an encoder and a decoder. The encoder is exploited to capture the context of the derived input images, while the decoder is employed to estimate the contribution of each input to the final dehazed result using the learned representations attributed to the encoder. The constructed network adopts a novel fusion-based strategy which derives three inputs from an original hazy image by applying White Balance (WB), Contrast Enhancing (CE), and Gamma Correction (GC). We compute pixel-wise confidence maps based on the appearance differences between these different inputs to blend the information of the derived inputs and preserve the regions with pleasant visibility. The final dehazed image is yielded by gating the important features of the derived inputs. To train the network, we introduce a multi-scale approach such that the halo artifacts can be avoided. Extensive experimental results on both synthetic and real-world images demonstrate that the proposed algorithm performs favorably against the state-of-the-art algorithms.

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)

Gated Fusion Network for Single Image Dehazing. / Ren, Wenqi; Ma, Lin; Zhang, Jiawei; Pan, Jinshan; Cao, Xiaochun; Liu, Wei; Yang, Ming-Hsuan.

Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2018). IEEE, 2018. p. 3253-3261 (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