Name
Abstract | ||
|---|---|---|
There are a large number of image super resolution algorithms based on the sparse coding, and some algorithms realize multi-frame super resolution. For utilizing multiple low resolution observations, 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 proposed formulation not only has a mathematically interesting structure called the double sparsity, but also offers improved numerical performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MLSP.2016.7738902 | 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Image Super Resolution,Sparse Coding,Double Sparsity | Pattern recognition,Neural coding,Computer science,Sparse approximation,Image coding,Artificial intelligence,Single objective,Superresolution,Image resolution,Image registration,Machine learning,Encoding (memory) | Conference |
ISSN | ISBN | Citations |
2161-0363 | 978-1-5090-0747-9 | 1 |
PageRank | References | Authors |
0.35 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toshiyuki Kato | 1 | 19 | 2.10 |
| Hideitsu Hino | 2 | 99 | 25.73 |
| Noboru Murata | 3 | 855 | 170.36 |