Compressive Sensing Reconstruction Solvers in Julia

While on Reddit, one of the reader ( Convex Optimization in Julia ) mentioned that he had done some implementations of some compressive sensing reconstruction solvers in Julia. He made those available on his GitHub:

https://github.com/dahlend/CompressedSensing

 

From the page: 

CompressedSensing

This package contains several useful algorithms for compressed sensing, multiple measurement vectors, and sparse blind source separation.

Available Algorithms

SMV - Single Measurement Vectors

• IRLS - Equality constrained Iteratively Rewieghted Least Squares Lp Minimization 1
• UIRLS - Unconstrained Iteratively Reweighted Lease Squares Lp Minimization 1

MMV - Multiple Measurement Vectors

• ZAP - Zeropoint Attractor 2

BSS - Sparse Blind Source Separation

• nGMCA - Sparse non-negative Blind Source Separation 3

Quantifying Sparsity

• GI - Absolute Gini Index 4
• Coherence - Measuring the coherence of a measurement matrix by the definitions commonly used 5

Documentation can be found at readthedocs here

 

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