Name
Abstract | ||
|---|---|---|
An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image, where correspondence between high- and low-resolution images are modeled by a certain degradation process. Relative displacements of small patches of observed low-resolution images are accurately estimated by a computationally efficient block matching method. The matching scores of the block matching are used to select a subset of low-resolution patches for reconstructing a high-resolution patch, that is, an adaptive selection of informative low-resolution images is realized. The proposed method is shown to perform comparable or superior to conventional super-resolution methods through experiments using various images. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neunet.2015.02.009 | Neural Networks |
Keywords | Field | DocType |
Image super resolution,Multi-frame super-resolution,Sparse coding | Computer vision,Pattern recognition,Neural coding,Adaptive selection,Artificial intelligence,Superresolution,Degradation process,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
66 | C | 0893-6080 |
Citations | PageRank | References |
12 | 0.65 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toshiyuki Kato | 1 | 19 | 2.10 |
| Hideitsu Hino | 2 | 99 | 25.73 |
| Noboru Murata | 3 | 855 | 170.36 |