An unsupervised image dehazing method using patch-line and fuzzy clustering-line priors

Miao Liao, Yan Lu, Xiong Li, Shuanhu Di, Wei Liang, Victor Chang

Research output: Contribution to journalArticlepeer-review


Outdoor images taken in haze usually exhibit contrast reduction, color distortion, and detail loss. Removing the haze from a given image is a tough issue owing to its highly ill-posed property. To restore the haze-free image effectively, we develop an unsupervised dehazing method using patch-line and fuzzy clustering-line priors in this paper. The method obtains the dehazed image by inversely solving the atmospheric scattering model, which involves in estimating two key parameters, including atmospheric light and scene transmission. First, the orientation of atmospheric light is achieved by using a patch-line prior. Then, a quadtree subspace hierarchical searching scheme is designed to get the magnitude by calculating the differences between the mean and variance of each component for local regions. Besides, a fuzzy clustering-line prior combined with a guided filtering is proposed to estimate the scene transmission for each pixel. The proposed method can obtain the dehazed image directly without any training process and achieve much better performance than many existing ones with less space and time cost.
Original languageEnglish
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Early online date4 Mar 2024
Publication statusE-pub ahead of print - 4 Mar 2024

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  • Atmospheric modeling
  • Estimation
  • Image color analysis
  • Image dehazing
  • Image restoration
  • Imaging
  • Reflection
  • Scattering
  • fuzzy clusteringline
  • patch-line
  • principal component analysis


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