Name
Abstract | ||
|---|---|---|
A large number of image super resolution algorithms based on the sparse coding are proposed, and some algorithms realize the multi-frame super resolution. In multi-frame super resolution based on the sparse coding, both accurate image registration and sparse coding are required. Previous study on multi-frame super resolution based on sparse coding firstly apply block matching for image registration, followed by sparse coding to enhance the image resolution. In this paper, these two problems are solved by optimizing a single objective function. The results of numerical experiments support the effectiveness of the proposed approch. |
Year | Venue | Field |
|---|---|---|
2015 | arXiv: Computer Vision and Pattern Recognition | Computer vision,Pattern recognition,Computer science,Neural coding,Sparse approximation,Artificial intelligence,Single objective,Image resolution,Superresolution,Machine learning,Image registration |
DocType | Volume | Citations |
Journal | abs/1512.00607 | 1 |
PageRank | References | Authors |
0.35 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toshiyuki Kato | 1 | 19 | 2.10 |
| Hideitsu Hino | 2 | 99 | 25.73 |
| Noboru Murata | 3 | 855 | 170.36 |