FSAD-Net: Feedback spatial attention dehazing network

Yu Zhou, Zhihua Chen, Ping Li, Haitao Song, C. L. Philip Chen, Bin Sheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

14 Citations (Scopus)


Recent dehazing networks learn more discriminative high-level features by designing deeper networks or introducing complicated structures, while ignoring inherent feature correlations in intermediate layers. In this article, we establish a novel and effective end-to-end dehazing method, named feedback spatial attention dehazing network (FSAD-Net). FSAD-Net is based on the recurrent structure and consists of four modules: a shallow feature extraction block (SFEB), a feedback block (FB), multiple advanced residual blocks (ARBs), and a reconstruction block (RB). FB is designed to handle feedback connections, and it can improve the dehazing performance by exploiting the dependencies of deep features across stages. ARB implements a novel attention-based estimation on a residual block to adapt to pixels with different distributions. Finally, RB helps restore haze-free images. It can be seen from the experimental results that FSAD-Net almost outperforms the state-of-the-arts in terms of five quantitative metrics. Moreover, the qualitatively comparisons on real-world images also demonstrate the superiority of the proposed FSAD-Net. Considering the efficiency and effectiveness of FSAD-Net, it can be expected to serve as a suitable image dehazing baseline in the future.

Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Neural Networks and Learning Systems
Publication statusAccepted/In press - Feb 2022


  • Atmospheric modeling
  • Correlation
  • Dehazing network
  • Feature extraction
  • Image color analysis
  • image dehazing
  • Image restoration
  • Indexes
  • recurrent structure
  • Scattering
  • spatial attention mechanism.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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