Abstract
In this paper, a new variational model for restoring blurred images with multiplicative noise is proposed. Based on the statistical property of the noise, a quadratic penalty function technique is utilized in order to obtain a strictly convex model under a mild condition, which guarantees the uniqueness of the solution and the stabilization of the algorithm. For solving the new convex variational model, a primal-dual algorithm is proposed, and its convergence is studied. The paper ends with a report on numerical tests for the simultaneous deblurring and denoising of images subject to multiplicative noise. A comparison with other methods is provided as well.
| Original language | English |
|---|---|
| Journal | S I A M Journal on Imaging Sciences |
| Volume | 6 |
| Issue number | 3 |
| Pages (from-to) | 1598-1625 |
| ISSN | 1936-4954 |
| DOIs | |
| Publication status | Published - 2013 |
Bibliographical note
© 2013 SIAM.Keywords
- Convexity
- Deblurring
- Multiplicative noise
- Primal-dual algorithm
- Total variation regularization
- Variational model
Fingerprint
Dive into the research topics of 'A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver