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Published 2007 | Published
Book Section - Chapter Open

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

Files

Candes2007p84222007_4Th_Ieee_International_Symposium_On_Biomedical_Imaging_Macro_To_Nano_Vols_1-3.pdf

Additional details

Created:
August 19, 2023
Modified:
October 20, 2023