AFF‐Dehazing: Attention‐based feature fusion network for low‐light image Dehazing

Yu Zhou, Zhihua Chen, Bin Sheng, Ping Li, Jinman Kim, Enhua Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively.

Original languageEnglish
Article numbere2011
Pages (from-to)1-12
JournalComputer Animation and Virtual Worlds
Issue number3-4
Publication statusPublished - Jul 2021


  • attention mechanism
  • image dehazing
  • low-light enhancement

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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