Skip to content

The source code for the CVPR 2016 paper "Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity".

License

Notifications You must be signed in to change notification settings

shahsohil/CoLaMP

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

CoLaMP

Introduction

This is a MATLAB implementation based of the CoLaMP algorithms presented in the following paper (download). "Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity", Sohil Atul Shah, Christoph Studer and Tom Goldstein

The source code and datasets are published under the MIT Licence. See LICENSE for details. In general you can use them for any purpose with proper attribution. If you do something interesting with the code, we'll be happy to know about it. Feel free to contact us.

Running Algorithms

As presented in the paper Algorithm 1 is implemented as part of block-sparse-RPCA code and Algorithm 2 is implemented in matching_pursuit.m. Apart from this, we also provide ADMM implementation for Algorithm 1 here. The denoising algorithm is implemented by plugging in Primal-Dual algorithm of this paper.

We have included test wrapper for all the four application in the codebase for the ease of understanding the input/output and for reproducing some of the paper's qualitative results.

About

The source code for the CVPR 2016 paper "Estimating Sparse Signals with Smooth Support via Convex Programming and Block Sparsity".

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages