Simplified non-locally dense network for single-image dehazing

Zhihua Chen, Zhuoliang Hu, Bin Sheng, Ping Li, Jinman Kim, Enhua Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


Single-image dehazing is an ill-posed problem. Most previous methods focused on estimating intermediate parameters for input hazy images. In this paper, we propose a novel end-to-end Simplified Non-locally Dense Network (SNDN) which does not rely on intermediate parameters. To capture long-range dependencies, we propose a Simplified Non-local Dense Block (SNDB) which is lightweight and outperforms traditional non-local method. Our SNDB will be embedded into a densely connected encoder–decoder network. To avoid gradients vanishing problem, we propose a simple branch network which only have five convolution layers. The effectiveness of our proposed network is proved through ablation experiment. In addition, we enhanced our training set by synthesizing colored hazy images, which helps restore the original color of the hazy image. The experimental results demonstrate that our network have better performance than most of the pervious state-of-the-art methods.

Original languageEnglish
Pages (from-to)2189-2200
Number of pages12
JournalVisual Computer
Issue number10-12
Publication statusPublished - Oct 2020


  • Dense
  • Non-local
  • Single-image dehazing

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


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