Name
Abstract | ||
|---|---|---|
Many techniques in signal and image processing exploit the sparsity of natural signals in a transform domain or dictionary. Adaptive synthesis dictionaries have been shown to be useful in applications such as signal denoising, and compressed sensing. More recently, the data-driven adaptation of sparsifying transforms has received some interest. The sparsifying transform model allows for exact and cheap computations. In this work, we propose a framework for online learning of square sparsifying transforms. Such online learning can be particularly useful when dealing with big data, and for signal processing applications such as realtime sparse representation and denoising. The proposed online transform learning algorithm is shown to have a much lower computational cost than online synthesis dictionary learning. The sequential learning of a sparsifying transform also typically converges faster than batch mode transform learning. Preliminary experiments show the usefulness of the proposed schemes for sparse representation, and denoising. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/GlobalSIP.2014.7032140 | Signal and Information Processing |
Keywords | Field | DocType |
learning (artificial intelligence),signal denoising,transforms,adaptive synthesis dictionaries,denoising,dictionary,image processing,natural signals,online sparsifying transform learning,signal processing,sparse representation,transform domain,Big data,Denoising,Dictionary learning,Online learning,Sparse representations,Sparsifying transforms | Noise reduction,Signal processing,Pattern recognition,Computer science,Sparse approximation,Image processing,Artificial intelligence,Batch processing,Sequence learning,Compressed sensing,Encoding (memory) | Conference |
Citations | PageRank | References |
2 | 0.38 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saiprasad Ravishankar | 1 | 587 | 36.58 |
| Bihan Wen | 2 | 225 | 18.64 |
| Yoram Bresler | 3 | 1104 | 119.17 |