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Journal of Electronic Imaging

Image deblurring with mixed regularization via the alternating direction method of multipliers
Author(s): Dongyu Yin; Ganquan Wang; Bin Xu; Dingbo Kuang
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Paper Abstract

In image deblurring problems, both local and nonlocal regularization priors are well studied. Local regularization prior assumes piecewise smoothness and transform-based sparsity, while the nonlocal one exploits self-similarity of images. We proposed a mixed regularization model which incorporates the advantages of both local adaptive sparsity prior and nonlocal sparsity prior resulting from the nonlocal self-similarity, and thus encourages a solution to simultaneously express both the local and nonlocal natures of images. The deblurring problem with mixed regularization can be transformed into a constrained optimization problem with separable structure via the variable splitting. Then this constrained optimization problem is solved by the alternating direction method of multipliers. Experimental results with a set of images under varying conditions demonstrate that the proposed method achieves the state-of-the-art deblurring performance.

Paper Details

Date Published: 25 August 2015
PDF: 10 pages
J. Electron. Imaging. 24(4) 043020 doi: 10.1117/1.JEI.24.4.043020
Published in: Journal of Electronic Imaging Volume 24, Issue 4
Show Author Affiliations
Dongyu Yin, Chinese Academy of Sciences (China)
Ganquan Wang, Shanghai Institute of Technical Physics (China)
Bin Xu, Tsinghua University (China)
Dingbo Kuang, Shanghai Institute of Technical Physics (China)

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