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:
From the page:
This package contains several useful algorithms for compressed sensing, multiple measurement vectors, and sparse blind source separation.
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
Documentation can be found at readthedocs here
- GI - Absolute Gini Index 4
- Coherence - Measuring the coherence of a measurement matrix by the definitions commonly used 5
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