Deblurring traffic sign images based on exemplars

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.

Improved estimation of motion blur parameters for restoration from a single image

“…Blind image deconvolution is an improved solution for motion blur image restoration, which firstly estimates two motion parameters from the blurred image and then restores the original appearance through the detected PSF. This kind of methods is clearly harder than non-blind counterpart and far more significant in estimating PSF, thus many researchers are focus on it [4,[23][24][25][26][27][28]. In order to result in an efficient iterative process, Figueiredo et al proposed a method based on the fast Fourier transform (FFT) and the discrete wavelet transform (DWT) to conduct the image restoration [24].…”