Abstract
Image restoration plays an important role in image processing, and numerous approaches have been proposed to tackle this problem. This paper presents a modified model for image restoration, that is based on a combination of Total Variation and Dictionary approaches. Since the well-known TV regularization is non-differentiable, the proposed method utilizes its dual formulation instead of its approximation in order to exactly preserve its properties. The data-fidelity term combines the one commonly used in image restoration and a wavelet thresholding based term. Then, the resulting optimization problem is solved via a first-order primal-dual algorithm. Numerical experiments demonstrate the good performance of the proposed model. In a last variant, we replace the classical TV by the nonlocal TV regularization, which results in a much higher quality of restoration.
Original language | English |
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Pages (from-to) | 507-535 |
Number of pages | 29 |
Journal | Inverse Problems and Imaging |
Volume | 8 |
Issue number | 2 |
DOIs | |
Publication status | Published - May 2014 |
Scopus Subject Areas
- Analysis
- Modelling and Simulation
- Discrete Mathematics and Combinatorics
- Control and Optimization
User-Defined Keywords
- Image restoration
- Nonlocal total variation
- Proximal gradient method
- Total variation
- Wavelet packet decomposition