Deblurring traffic sign images based on exemplars

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.

[1]  Sunghyun Cho,et al.  Fast motion deblurring , 2009, SIGGRAPH 2009.

[2]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[3]  Frédo Durand,et al.  Image and depth from a conventional camera with a coded aperture , 2007, SIGGRAPH 2007.

[4]  Li Xu,et al.  Two-Phase Kernel Estimation for Robust Motion Deblurring , 2010, ECCV.

[5]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[6]  Ming-Hsuan Yang,et al.  Deblurring Face Images with Exemplars , 2014, ECCV.

[7]  Dani Lischinski,et al.  Deblurring by Example Using Dense Correspondence , 2013, 2013 IEEE International Conference on Computer Vision.

[8]  Ming-Hsuan Yang,et al.  $L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Michal Irani,et al.  Blind Deblurring Using Internal Patch Recurrence , 2014, ECCV.

[10]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[11]  Saturnino Maldonado-Bascón,et al.  Goal Evaluation of Segmentation Algorithms for Traffic Sign Recognition , 2010, IEEE Transactions on Intelligent Transportation Systems.

[12]  Zhixun Su,et al.  Kernel estimation from salient structure for robust motion deblurring , 2012, Signal Process. Image Commun..

[13]  Deqing Sun,et al.  Blind Image Deblurring Using Dark Channel Prior , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, CVPR.

[15]  Johannes Stallkamp,et al.  Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition , 2012, Neural Networks.

[16]  Jiaya Jia,et al.  Single Image Motion Deblurring Using Transparency , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Frédo Durand,et al.  Efficient marginal likelihood optimization in blind deconvolution , 2011, CVPR 2011.

[18]  Cewu Lu,et al.  Image smoothing via L0 gradient minimization , 2011, ACM Trans. Graph..

[19]  Jiaya Jia,et al.  Mathematical models and practical solvers for uniform motion deblurring , 2014, Motion Deblurring.

[20]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[21]  Meng-Yin Fu,et al.  A survey of traffic sign recognition , 2010, 2010 International Conference on Wavelet Analysis and Pattern Recognition.

[22]  Richard Szeliski,et al.  PSF estimation using sharp edge prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Seungyong Lee,et al.  Recent advances in image deblurring , 2013, SA '13.