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An Augmented Lagrangian Method for Image Restoration Problems
Stanley H. Chan
Video Processing Lab, Department of Electrical and Computer Engineering , UCSD
Abstract:
This talk concerns the classical total variation (TV) image deblurring problems, which involves an unconstrained minimization problem consisting of a least-squares term and a total variation regularization term. We transform the original unconstrained problem into an equivalent constrained problem, and use an augmented Lagrangian method to handle the constraints. The transformation allows the differentiable and non-differentiable parts of the objective function to be treated using separate subproblems. Each subproblem may be solved efficiently and an alternating strategy is used to combine the solutions. The new algorithm is faster than several state-of-the-art TV algorithms.
Tuesday, May 25, 2010
11:00AM AP&M 2402
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Stanley H. Chan
Video Processing Lab, Department of Electrical and Computer Engineering , UCSD
Abstract:
This talk concerns the classical total variation (TV) image deblurring problems, which involves an unconstrained minimization problem consisting of a least-squares term and a total variation regularization term. We transform the original unconstrained problem into an equivalent constrained problem, and use an augmented Lagrangian method to handle the constraints. The transformation allows the differentiable and non-differentiable parts of the objective function to be treated using separate subproblems. Each subproblem may be solved efficiently and an alternating strategy is used to combine the solutions. The new algorithm is faster than several state-of-the-art TV algorithms.
Tuesday, May 25, 2010
11:00AM AP&M 2402