An Efficient Variational Method for Image Restoration | Zendy

Jun Liu | Zendy; TingZhu Huang | Zendy; Xiao-Guang Lv | Zendy; Si Wang | Zendy

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An Efficient Variational Method for Image Restoration

Author(s) -

Jun Liu,

TingZhu Huang,

Xiao-Guang Lv,

Si Wang

Publication year - 2013

Publication title -

abstract and applied analysis

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.228

H-Index - 56

eISSN - 1687-0409

pISSN - 1085-3375

DOI - 10.1155/2013/213536

Subject(s) - mathematics , regularization (linguistics) , total variation denoising , image restoration , minification , convergence (economics) , image (mathematics) , algorithm , mathematical optimization , similarity (geometry) , property (philosophy) , image processing , artificial intelligence , computer science , philosophy , epistemology , economic growth , economics

Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM)

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