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Deblurring traffic sign images based on exemplars

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  • Houjie Li
  • Tianshuang Qiu
  • Shengyang Luan
  • Haiyu Song
  • Linxiu Wu

Abstract

Motion blur appearing in traffic sign images may lead to poor recognition results, and therefore it is of great significance to study how to deblur the images. In this paper, a novel method for deblurring traffic sign is proposed based on exemplars and several related approaches are also made. First, an exemplar dataset construction method is proposed based on multiple-size partition strategy to lower calculation cost of exemplar matching. Second, a matching criterion based on gradient information and entropy correlation coefficient is also proposed to enhance the matching accuracy. Third, L0.5-norm is introduced as the regularization item to maintain the sparsity of blur kernel. Experiments verify the superiority of the proposed approaches and extensive evaluations against state-of-the-art methods demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Houjie Li & Tianshuang Qiu & Shengyang Luan & Haiyu Song & Linxiu Wu, 2018. "Deblurring traffic sign images based on exemplars," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-19, March.
    • Handle: RePEc:plo:pone00:0191367
    DOI: 10.1371/journal.pone.0191367
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    Cited by:

    1. Wei Zhou & Xingxing Hao & Kaidi Wang & Zhenyang Zhang & Yongxiang Yu & Haonan Su & Kang Li & Xin Cao & Arjan Kuijper, 2020. "Improved estimation of motion blur parameters for restoration from a single image," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.

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