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...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2014-01-01
|
| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2014/906464 |
| Summary: | 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. |
|---|---|
| ISSN: | 1687-5680 1687-5699 |