compressed Sensing Reconstruction via Belief Propagation: Difference between revisions

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==Conclusion==
==Conclusion==
The compressive sensing algorithm can be applied to analog signals as well. This sensing technique finds many practical applications in image processing and similar fields. In this summary, we learned about compressive sensing which is a more efficient method compared to the traditional transform coding of signals that uses a sample-then-compress framework.


==References==
==References==

Revision as of 16:52, 30 October 2011

Introduction

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

<references />

5. Richard G. Baraniuk. Compressive Sensing.

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