This page is dedicated to codes I implemented on the subject of Compressed Sensing. A better implementation is surely available from the authors, please contact them for true and tried implementations. Other codes can be found on the Monday Morning Algorithm Series page. The Nuit Blanche blog on Compressed Sensing is here. General Codes for Compressed Sensing can be found here or at the Rice Compressed Sensing Repository. I am trying, as much as I can, to make sure these codes work with Octave.
1. Reconstruction codes
[ Warning the following two versions may not be optimal:
2. cosamp.m implemented by David Mary as described in CoSaMP: Iterative signal recovery from incomplete and inaccurate samples by Deanna Needell and Joel Tropp.
* Subspace Pursuit for Compressive Sensing
2. [Warning this version of the code is NOT optimal, please use the code provided below by the authors] The code is here. Version 0.1, March 13th, 2008. The algorithm was introduced here and implemented here. The algorithm is implemented from directions given in Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity by Wei Dai and Olgica Milenkovic.
1. Lp_re.m ,Version 0.1, Feb 8th, 2008. It was implemented from Iteratively Reweighted Algorithms for Compressive Sensing by Rick Chartrand and Wotao Yin.
1.cspocs_K.m, can be found in this zip file. This algorithm was discovered and implemented by Laurent Jacques. The algorithm is discussed in this entry (Monday Morning Algorithm 16: S-POCS, Using Non Convex Sparse Signal Space Instead of the Convex l1 Ball Improves POCS Recovery).
* Smoothed L0 norm algorithm
1. sl0.m as provided G. Hosein Mohimani and described in Fast Sparse Representation using Smoothed L0 Norm by G. Hosein Mohimani, Massoud Babaie-Zadeh and Christian Jutten. Article submitted to IEEE Trans. on signal processing. [ Important Update: The authors now have a site with a probably updated version of the algorithm, please visit the Smoothed L0 (SL0) Algorithm for Sparse Decomposition site ]
* Subspace Pursuit + Reweighted Lp reconstruction algorithm for Compressive Sensing
2. Measurement Matrices / Encoding
* Sparse Matrix Measurement
* Scrambled Block Hadamard Ensemble
Implemented from Fast compressive imaging using scrambled block Hadamard ensemble by Lu Gan, Thong Do and Trac Tran.
* Compressed Sensing Meets Machine Learning
The script is here. More explanation are given in Compressed Sensing meets Machine Learning /
Recognition via Sparse Representation Classification Algorithm, an entry and script written by
* Comparison between various reconstruction codes:
* Other codes implemented in the Monday Morning Algorithm series can be found here.Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License.