Monday, November 17, 2014

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:


From the page: 


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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