compressed Sensing Reconstruction via Belief Propagation: Difference between revisions
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==Introduction== | ==Introduction== | ||
One of the key theorem in digital signal processing is [http://www.dynamicmeasurementsolutions.com/Articles/SV_0202lago.pdf Shannon/Nyquist] theorem. | One of the key theorem in digital signal processing is [http://www.dynamicmeasurementsolutions.com/Articles/SV_0202lago.pdf Shannon/Nyquist] theorem. This theorem specifies the conditions on which a band limited signal can be reconstructed uniquely from its discrete samples. This property of band limited signals made signal processing viable on natural analog signals. However, in some applications even the sampled signal lays in a extremely high dimensional vector space. To make signal processing algorithms computationally tractable, many researchers work on compressiblity of signals. It is assumed that most of the information content of a signal | ||
==Compressed Sensing== | ==Compressed Sensing== |
Revision as of 17:56, 30 October 2011
Introduction
One of the key theorem in digital signal processing is Shannon/Nyquist theorem. This theorem specifies the conditions on which a band limited signal can be reconstructed uniquely from its discrete samples. This property of band limited signals made signal processing viable on natural analog signals. However, in some applications even the sampled signal lays in a extremely high dimensional vector space. To make signal processing algorithms computationally tractable, many researchers work on compressiblity of signals. It is assumed that most of the information content of a signal
Compressed Sensing
Compressed Sensing Reconstruction Algorithms
Connecting CS decoding to graph decoding algorithms
CS-LDPC decoding of sparse signals
Source model
Decoding via statistical inference
Exact solution to CS statistical inference
Approximate solution to CS statistical inference via message passing
Numerical results
Conclusion
References
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