Journal of Xidian University ›› 2023, Vol. 50 ›› Issue (2): 23-32.doi: 10.19665/j.issn1001-2400.2023.02.003

• nformation and Communications Engineering • Previous Articles     Next Articles

Low-light image dehazing network with aggregated context-aware attention

WANG Keyan(),CHENG Jicong(),HUANG Shirui(),CAI Kunlun(),WANG Weiran(),LI Yunsong()   

  1. State Key Laboratory of Integrated Services Networks,Xidian University,Xi’an 710071,China
  • Received:2022-06-27 Online:2023-04-20 Published:2023-05-12


Existing low-light dehazing algorithms are affected by the low and uneven illumination of the hazy images with their dehazed images often suffering from loss of details and color distortion.To address the above problems,a low-light image dehazing network with aggregated context-aware attention (ACANet) is proposed.First,an intra-layer context-aware attention module is introduced to identify and highlight significant features at the same scale from the channel dimension and the spatial dimension,respectively,so that the network can break through the constraints of the local field of view,and extract image texture information more efficiently.Second,an inter-layer context-aware attention module is introduced to efficiently fuse multi-scale features and the advanced features are mapped to the signal subspace through projection operations in order to further enhance the reconstruction of image details.Finally,the CIEDE2000 color shift loss function is adopted to constrain the image hue by CIELAB color space and jointly optimize the network together with L2 loss so as to enable the network to learn image colors accurately and solve the severe color shift problem.Both quantitative and qualitative experimental results on several datasets demonstrate that the proposed ACANet outperforms existing dehazing methods.Specifically,the ACANet improves the PSNR of dehazed images by 8.8% compared to the baseline network,and enhances the image visibility with richer details and more natural color.

Key words: low-light image dehazing, attention mechanism, feature fusion, color shift loss, deep learning

CLC Number: 

  • TP391.41