Projects per year
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 sampledistortion 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 multiresolution 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 

Title of host publication  Communication, Control, and Computing (Allerton), 2011 49th Annual Allerton Conference on 
Pages  902908 
Number of pages  7 
DOIs  
Publication status  Published  2011 
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Projects
 2 Finished

Extensions to compressed sensing theory with application to dynamic MRI
1/03/09 → 31/03/12
Project: Research
