Published 2007
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Sparse signal and image recovery from Compressive Samples
Abstract
In this paper we present an introduction to Compressive Sampling (CS), an emerging model-based framework for data acquisition and signal recovery based on the premise that a signal having a sparse representation in one basis can be reconstructed from a small number of measurements collected in a second basis that is incoherent with the first. Interestingly, a random noise-like basis will suffice for the measurement process. We will overview the basic CS theory, discuss efficient methods for signal reconstruction, and highlight applications in medical imaging.
Additional Information
© 2007 IEEE. This work has been partially supported by National Science Foundation (NSF) grants ITR ACI-0204932 and CCF515362, NSF fellowship DMS- 0603606, and the 2006 NSF Waterman Award.Attached Files
Published - Candes2007p84222007_4Th_Ieee_International_Symposium_On_Biomedical_Imaging_Macro_To_Nano_Vols_1-3.pdf
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Candes2007p84222007_4Th_Ieee_International_Symposium_On_Biomedical_Imaging_Macro_To_Nano_Vols_1-3.pdf
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Additional details
- Eprint ID
- 19543
- Resolver ID
- CaltechAUTHORS:20100820-090253640
- ITR ACI-0204932
- NSF
- CCF515362
- NSF
- DMS-0603606
- NSF fellowship
- 2006 NSF Waterman Award
- Created
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2010-08-20Created from EPrint's datestamp field
- Updated
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2021-11-08Created from EPrint's last_modified field