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Image deblurring with mixed regularization via the alternating direction method of multipliers

[+] Author Affiliations
Dongyu Yin, Ganquan Wang, Dingbo Kuang

Chinese Academy of Sciences, Shanghai Institute of Technical Physics, Key Laboratory of Infrared System Detection and Imaging Technology, No. 500 Yutian Road, Hongkou District, Shanghai 200083, China

Bin Xu

Tsinghua University, Department of Electronic Engineering, Room 10-101, Rohm Building, Haidian District, Beijing 100084, China

J. Electron. Imaging. 24(4), 043020 (Aug 25, 2015). doi:10.1117/1.JEI.24.4.043020
History: Received April 23, 2015; Accepted July 24, 2015
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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.

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Citation

Dongyu Yin ; Ganquan Wang ; Bin Xu and Dingbo Kuang
"Image deblurring with mixed regularization via the alternating direction method of multipliers", J. Electron. Imaging. 24(4), 043020 (Aug 25, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.4.043020


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