Blind motion image deblurring using nonconvex higher-order total variation model

Weihong Li; Rui Chen; Shangwen Xu; Weiguo Gong

doi:10.1117/1.JEI.25.5.053033

19 October 2016 Blind motion image deblurring using nonconvex higher-order total variation model

Weihong Li, Rui Chen, Shangwen Xu, Weiguo Gong

Author Affiliations +

Journal of Electronic Imaging, Vol. 25, Issue 5, 053033 (October 2016). https://doi.org/10.1117/1.JEI.25.5.053033

Abstract

We propose a nonconvex higher-order total variation (TV) method for blind motion image deblurring. First, we introduce a nonconvex higher-order TV differential operator to define a new model of the blind motion image deblurring, which can effectively eliminate the staircase effect of the deblurred image; meanwhile, we employ an image sparse prior to improve the edge recovery quality. Second, to improve the accuracy of the estimated motion blur kernel, we use L₁ norm and H¹ norm as the blur kernel regularization term, considering the sparsity and smoothing of the motion blur kernel. Third, because it is difficult to solve the numerically computational complexity problem of the proposed model owing to the intrinsic nonconvexity, we propose a binary iterative strategy, which incorporates a reweighted minimization approximating scheme in the outer iteration, and a split Bregman algorithm in the inner iteration. And we also discuss the convergence of the proposed binary iterative strategy. Last, we conduct extensive experiments on both synthetic and real-world degraded images. The results demonstrate that the proposed method outperforms the previous representative methods in both quality of visual perception and quantitative measurement.

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Weihong Li, Rui Chen, Shangwen Xu, and Weiguo Gong "Blind motion image deblurring using nonconvex higher-order total variation model," Journal of Electronic Imaging 25(5), 053033 (19 October 2016). https://doi.org/10.1117/1.JEI.25.5.053033

Published: 19 October 2016

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