Split Bregman Iteration Algorithm for Image Deblurring Using Fourth-Order Total Bounded Variation Regularization Model | Zendy

Yi Xu | Zendy; TingZhu Huang | Zendy; Jun Liu | Zendy; Xiao-Guang Lv | Zendy

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Split Bregman Iteration Algorithm for Image Deblurring Using Fourth-Order Total Bounded Variation Regularization Model

Author(s) -

Yi Xu,

TingZhu Huang,

Jun Liu,

Xiao-Guang Lv

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/238561

Subject(s) - deblurring , total variation denoising , regularization (linguistics) , bounded function , variation (astronomy) , mathematics , bounded variation , algorithm , convergence (economics) , mathematical optimization , image (mathematics) , image restoration , computer science , image processing , artificial intelligence , mathematical analysis , physics , economic growth , astrophysics , economics

We propose a fourth-order total bounded variation regularization model which could reduce undesirable effects effectively. Based on this model, we introduce an improved split Bregman iteration algorithm to obtain the optimum solution. The convergence property of our algorithm is provided. Numerical experiments show the more excellent visual quality of the proposed model compared with the second-order total bounded variation model which is proposed by Liu and Huang (2010)

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