The STOne Transform : Multi-Resolution Image Enhancement and Compressive Video
Compressive sensing enables the reconstruction of high-resolution signals from under-sampled data. While the compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This paper presents a new sensing framework...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 24(2015), 12 vom: 23. Dez., Seite 5581-93 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2015
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. |
| Zusammenfassung: | Compressive sensing enables the reconstruction of high-resolution signals from under-sampled data. While the compressive methods simplify data acquisition, they require the solution of difficult recovery problems to make use of the resulting measurements. This paper presents a new sensing framework that combines the advantages of both the conventional and the compressive sensing. Using the proposed sum-to-one transform, the measurements can be reconstructed instantly at the Nyquist rates at any power-of-two resolution. The same data can then be enhanced to higher resolutions using the compressive methods that leverage sparsity to beat the Nyquist limit. The availability of a fast direct reconstruction enables the compressive measurements to be processed on small embedded devices. We demonstrate this by constructing a real-time compressive video camera |
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| Beschreibung: | Date Completed 03.02.2016 Date Revised 27.01.2016 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2015.2474697 |