CASIA OpenIR  > 智能制造技术与系统研究中心  > 多维数据分析
Robust image deblurring using hyper Laplacian model
Xu Yuquan; Hu Xiyuan; Peng Silong
Conference Name11th Asian Conference on Computer Vision ACCV 2012
Pagespp 49-60
Conference Date2012/11/5-2012/11/6
Conference Place韩国Daejeon Korea Republic of Korea
AbstractIn recent years many image deblurring algorithms have been proposed most of which assume the noise in the deblurring process satisfies the Gaussian distribution. However it is often unavoidable in practice both in nonblind and blind image deblurring due to the error on the input kernel and the outliers in the blurry image. Without proper handing these outliers the recovered image estimated by previous methods will suffer severe artifacts. In this paper we mainly deal with two kinds of non-Gaussian noise in the image deblurring process inaccurate kernel and compressed blurry image and find that handling the noise as Laplacian distribution can get more robust result in these cases. Based on this point the new non-blind and blind image deblurring algorithms are proposed to restore the clear image. To get more robust deblurred result we also use 8 direction gradients of the image to estimate the blur kernel. The new minimization problem can be efficiently solved by the Iteratively Reweighted Least Squares(IRLS) and the experimental results on both synthesized and real-world images show the efficiency and robustness of our algorithm.
Document Type会议论文
Corresponding AuthorXu Yuquan
Recommended Citation
GB/T 7714
Xu Yuquan,Hu Xiyuan,Peng Silong. Robust image deblurring using hyper Laplacian model[C],2013:pp 49-60.
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