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2829. A Scaled Gradient Projection method for the realization of the Balancing Principle in TGV-based image restoration
Invited abstract in session TA-32: Nonsmooth optimization and applications, Part I, stream Advances in large scale nonlinear optimization.
Tuesday, 8:30-10:00Room: 41 (building: 303A)
Authors (first author is the speaker)
| 1. | Germana Landi
|
| Department of Mathematics, University of Bologna | |
| 2. | Marco Viola
|
| School of Mathematical Sciences, Dublin City University | |
| 3. | Fabiana Zama
|
| Department of Mathematics, University of Bologna |
Abstract
In the last few years, Total Generalized Variation (TGV) regularization has proved to be a valuable tool to remove blur and noise from an image while avoiding the staircase effect and preserving the sharp edges. The TGV model depends on two regularization parameters whose values must be appropriately selected to obtain good-quality restored images.
In this work, we use the Balancing Principle (BP) to formulate the TGV-based image restoration problem as a constrained minimization problem whose objective is an implicit function of the two regularization parameters depending on the image to be restored. The values of the regularization parameters, and the corresponding restored image, satisfying the optimality condition of the formulated problem guarantee that the data fidelity and regularization terms are balanced. A Scaled Gradient projection method is proposed specifically tailored to the BP-based optimization problem. The numerical results show that the proposed approach can effectively restore input images corrupted by several kinds of noise.
Keywords
- Non-smooth Optimization
Status: accepted
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