Name
Abstract | ||
|---|---|---|
Multiframe super-resolution algorithms can be used to reconstruct a high-quality high-resolution image from several warped, blurred, undersampled, and possibly noisy images. A widely used means of implementing such algorithms is by optimization-based model inversion. In the past, steepest-descent methods have been applied. While easy to implement, these methods are known for their poor convergence properties and for being sensitive to numerical ill-conditioning. In this paper, we show that the multiframe super-resolution problem can be solved by using quasi-Newton algorithms and propose efficient implementations. Two of these algorithms were applied to a known super-resolution scheme and preliminary results obtained show a significant improvement in terms of convergence speed. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ISCAS.2008.4541405 | Seattle, WA |
Keywords | Field | DocType |
Newton method,image reconstruction,image resolution,optimisation,image reconstruction,multiframe image super-resolution,optimization-based model inversion,quasi Newton algorithms,Image processing,multiframe reconstruction,quasi-Newton optimization,super-resolution | Convergence (routing),Iterative reconstruction,Computer vision,Model inversion,Computer science,Image processing,Algorithm,Robustness (computer science),Artificial intelligence,Superresolution,Image resolution,Newton's method | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-4244-1684-4 | 1 |
PageRank | References | Authors |
0.40 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diego A. Sorrentino | 1 | 1 | 0.40 |
| A. Antoniou | 2 | 267 | 30.79 |