文章基本信息

标题：The lo-norm-based Blind Image Deconvolution: Comparison and Inspiration
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作者：Hai-Song Deng ; Wen-Ze Shao
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2016
卷号：13
期号：6
出版社：IJCSI Press
摘要：Single image blind deblurring has been intensively studied since Fergus et al.s variational Bayes method in 2006. It is now commonly believed that the blur-kernel estimation accuracy is highly dependent on the pursed salient edge information from the blurred image, which stimulates numerous l0-approximating blind deblurring methods via kinds of techniques and tricks. This paper, however, focuses on the four recent daring attempts which are all based on the simple and direct lo-norm. A systematic com- parative analysis is made towards those methods, clarifying their similarities and differences, and providing a benchmark evaluation on both the deblurring quality and computational efficiency. Results have demonstrated that the lo-norm alone is far enough to achieve top blind deblurring performance. Instead, details are to be paid with fairly more attention as working on the problem formulation as well as the algorithmic deduction. Inspired by the success of the bi-lo-l2-norm regularization, an attempt has been made to boost a recently proposed normalized sparsity-based blind deblurring method via simply borrowing core ideas behind the bi-lo-l2-norm regularization. Experimental results show that the boosting approach has leaded to a significant improvement in terms of both accuracy and efficiency. Finally, several possible extensions are discussed towards the bi-lo-l2-norm regularization.
关键词：blind deblurring; camera shake removal; variational Bayes; lo;norm minimization; split Bregman; half;quadratic