compressed Sensing Reconstruction via Belief Propagation
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
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
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5. Richard G. Baraniuk. Compressive Sensing.