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

Dongyu Yin; Ganquan Wang; Bin Xu; Dingbo Kuang

doi:10.1117/1.JEI.24.4.043020

25 August 2015 Image deblurring with mixed regularization via the alternating direction method of multipliers

Dongyu Yin, Ganquan Wang, Bin Xu, Dingbo Kuang

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 4, 043020 (August 2015). https://doi.org/10.1117/1.JEI.24.4.043020

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

Published: 25 August 2015

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