Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method
Image deblurring is formulated as an unconstrained minimization problem, and its penalty function is the sum of the error term and TVp-regularizers with 0<p<1. Although TVp-regularizer is a powerful tool that can significantly promote the sparseness of image gradients, it is neither convex nor...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2014/906464 |
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| _version_ | 1832551467684200448 |
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| author | Su Xiao |
| author_facet | Su Xiao |
| author_sort | Su Xiao |
| collection | DOAJ |
| description | Image deblurring is formulated as an unconstrained minimization problem, and its penalty function is the sum of the error term and TVp-regularizers
with 0<p<1. Although TVp-regularizer is a powerful tool that can significantly promote the sparseness of image gradients, it is neither convex nor smooth, thus making the
presented optimization problem more difficult to deal with. To solve this minimization
problem efficiently, such problem is first reformulated as an equivalent constrained minimization problem by introducing new variables and new constraints. Thereafter, the split
Bregman method, as a solver, splits the new constrained minimization problem into subproblems. For each subproblem, the corresponding efficient method is applied to ensure
the existence of closed-form solutions. In simulated experiments, the proposed algorithm
and some state-of-the-art algorithms are applied to restore three types of blurred-noisy
images. The restored results show that the proposed algorithm is valid for image deblurring and is found to outperform other algorithms in experiments. |
| format | Article |
| id | doaj-art-69738aebf31548b59712d17d270ed980 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-69738aebf31548b59712d17d270ed9802025-02-03T06:01:27ZengWileyAdvances in Multimedia1687-56801687-56992014-01-01201410.1155/2014/906464906464Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman MethodSu Xiao0School of Computer Science and Technology, Huaibei Normal University, Huaibei 235000, ChinaImage deblurring is formulated as an unconstrained minimization problem, and its penalty function is the sum of the error term and TVp-regularizers with 0<p<1. Although TVp-regularizer is a powerful tool that can significantly promote the sparseness of image gradients, it is neither convex nor smooth, thus making the presented optimization problem more difficult to deal with. To solve this minimization problem efficiently, such problem is first reformulated as an equivalent constrained minimization problem by introducing new variables and new constraints. Thereafter, the split Bregman method, as a solver, splits the new constrained minimization problem into subproblems. For each subproblem, the corresponding efficient method is applied to ensure the existence of closed-form solutions. In simulated experiments, the proposed algorithm and some state-of-the-art algorithms are applied to restore three types of blurred-noisy images. The restored results show that the proposed algorithm is valid for image deblurring and is found to outperform other algorithms in experiments.http://dx.doi.org/10.1155/2014/906464 |
| spellingShingle | Su Xiao Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method Advances in Multimedia |
| title | Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method |
| title_full | Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method |
| title_fullStr | Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method |
| title_full_unstemmed | Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method |
| title_short | Deblurring by Solving a TVp-Regularized Optimization Problem Using Split Bregman Method |
| title_sort | deblurring by solving a tvp regularized optimization problem using split bregman method |
| url | http://dx.doi.org/10.1155/2014/906464 |
| work_keys_str_mv | AT suxiao deblurringbysolvingatvpregularizedoptimizationproblemusingsplitbregmanmethod |