@article{ref1,
title="Deblurring traffic sign images based on exemplars",
journal="PLoS one",
year="2018",
author="Li, Houjie and Qiu, Tianshuang and Luan, Shengyang and Song, Haiyu and Wu, Linxiu",
volume="13",
number="3",
pages="e0191367-e0191367",
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.<p /> <p>Language: en</p>",
language="en",
issn="1932-6203",
doi="10.1371/journal.pone.0191367",
url="http://dx.doi.org/10.1371/journal.pone.0191367"
}