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Abstract
We consider compressed sensing within a stochastic setting, where the signal or image of interest is drawn from a probability distribution that is in some sense compressible. Within this setting we consider some sample-distortion functions for i.i.d. compressible distributions and derive a simple sample distortion lower bound. We then extend the compressible model to consider a stochastic multi-resolution image model. Using empirical sample distortion functions we are able to compute an optimal bandwise sampling strategy and to accurately predict the compressed sensing possible performance gains available in compressive imaging.
Original language | English |
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Title of host publication | Communication, Control, and Computing (Allerton), 2011 49th Annual Allerton Conference on |
Pages | 902-908 |
Number of pages | 7 |
DOIs | |
Publication status | Published - 2011 |
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Dive into the research topics of 'Sample-distortion functions for compressed sensing'. Together they form a unique fingerprint.Projects
- 2 Finished
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Extensions to compressed sensing theory with application to dynamic MRI
1/03/09 → 31/03/12
Project: Research
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