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ICIP
2009
IEEE
13 years 7 months ago
Informative sensing of natural images
The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
Hyun Sung Chang, Yair Weiss, William T. Freeman
PERCOM
2010
ACM
13 years 8 months ago
Resilient image sensor networks in lossy channels using compressed sensing
—Data loss in wireless communications greatly affects the reconstruction quality of a signal. In the case of images, data loss results in a reduction in quality of the received i...
Scott Pudlewski, Arvind Prasanna, Tommaso Melodia
ICIP
2009
IEEE
13 years 7 months ago
Randomness-in-Structured Ensembles for compressed sensing of images
Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., ...
Abdolreza A. Moghadam, Hayder Radha
CORR
2010
Springer
165views Education» more  CORR 2010»
13 years 9 months ago
Compressed Sensing for Sparse Underwater Channel Estimation: Some Practical Considerations
We examine the use of a structured thresholding algorithm for sparse underwater channel estimation using compressed sensing. This method shows some improvements over standard algo...
Sushil Subramanian
TIT
2010
112views Education» more  TIT 2010»
13 years 4 months ago
Exponential bounds implying construction of compressed sensing matrices, error-correcting codes, and neighborly polytopes by ran
In [12] the authors proved an asymptotic sampling theorem for sparse signals, showing that n random measurements permit to reconstruct an N-vector having k nonzeros provided n >...
David L. Donoho, Jared Tanner