Name
Abstract | ||
|---|---|---|
Sparse signal representation based on overcomplete dictionaries has recently been extensively investigated, rendering the state-of-the-art results in signal, image and video processing. We propose a novel dictionary learning algorithm-the PK-SVD algorithm-which assumes prior probabilities on the dictionary atoms and learns a sparse dictionary under a popularity-based model. The prior distribution brings the flexibility that is desirable in applications. We examine our algorithm in both synthetic tests and image denoising experiments. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946685 | 2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING |
Keywords | Field | DocType |
Dictionary learning, sparse representation, K-SVD, PK-SVD, OMP | Singular value decomposition,Video processing,K-SVD,Pattern recognition,Computer science,Sparse approximation,Artificial intelligence,Rendering (computer graphics),Prior probability,Sparse matrix,Machine learning,Encoding (memory) | Conference |
Volume | Issue | ISSN |
null | null | 1520-6149 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| JianZhou Feng | 1 | 23 | 3.61 |
| Li Song | 2 | 323 | 65.87 |
| Xiaoming Huo | 3 | 157 | 24.83 |
| Xiaokang Yang | 4 | 3581 | 238.09 |
| Wenjun Zhang | 5 | 1789 | 177.28 |