Name
Abstract | ||
|---|---|---|
The reconstruction performance of an orthogonal matching pursuit algorithm is poor due to less observation values. An observation matrix design method which can adaptively ensure the sample size based on the image information is proposed. To make the algorithm more sparsely representative, an adaptive orthogonal matching pursuit algorithm based on the redundant dictionary is discussed by using a K-SVD dictionary training method to get a sparse dictionary. Experimental results show that the algorithm not only solves the problem that the sample size is small, but also improves the image reconstruction quality. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FSKD.2013.6816263 | 2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) |
Keywords | Field | DocType |
orthogonal matching pursuit, redundancy dictionary, sparse coding | Iterative reconstruction,Matching pursuit,Algorithm design,K-SVD,Pattern recognition,Computer science,Sparse approximation,Redundancy (engineering),Artificial intelligence,Compressed sensing,Machine learning,Sparse matrix | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yumin Tian | 1 | 2 | 2.13 |
| Zhihui Wang | 2 | 0 | 0.34 |