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IPAS
2007
15 years 1 months ago
Prediction of signs of DCT coefficients in block-based lossy image compression
A practical impossibility of prediction of signs of DCT coefficients is generally accepted. Therefore each coded sign of DCT coefficients occupies usually 1 bit of memory in compr...
Nikolay N. Ponomarenko, Andriy V. Bazhyna, Karen O...
PERCOM
2010
ACM
14 years 9 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
JDCTA
2010
188views more  JDCTA 2010»
14 years 6 months ago
Compressive Sensing Signal Detection Algorithm Based on Location Information of Sparse Coefficients
Without reconstructing the signal themselves, signal detection could be solved by detection algorithm, which directly processes sampling value obtained from compressive sensing si...
Bing Liu, Ping Fu, Shengwei Meng, Lunping Guo
ICASSP
2011
IEEE
14 years 2 months ago
Combined compressed sensing and parallel mri compared for uniform and random cartesian undersampling of K-space
Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater und...
Daniel S. Weller, Jonathan R. Polimeni, Leo Grady,...
SIAMIS
2011
14 years 6 months ago
Gradient-Based Methods for Sparse Recovery
The convergence rate is analyzed for the sparse reconstruction by separable approximation (SpaRSA) algorithm for minimizing a sum f(x) + ψ(x), where f is smooth and ψ is convex, ...
William W. Hager, Dzung T. Phan, Hongchao Zhang