compressed Sensing Reconstruction via Belief Propagation

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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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