Computer Graphics
TU Braunschweig

A Sparse Kaczmarz Solver and a Linearized Bregman Method for Online Compressed Sensing


A Sparse Kaczmarz Solver and a Linearized Bregman Method for Online Compressed Sensing

An algorithmic framework to compute sparse or minimal-TV solutions of linear systems is proposed. The framework includes both the Kaczmarz method and the linearized Bregman method as special cases and also several new methods such as a sparse Kaczmarz solver. The algorithmic framework has a variety of applications and is especially useful for problems in which the linear measurements are slow and expensive to obtain. We present examples for online compressed sensing, TV tomographic reconstruction and radio interferometry.


Author(s):Dirk Lorenz, Stephan Wenger, Frank Schöpfer, Marcus Magnor
Published:October 2014
Type:Article in conference proceedings
Book:Proc. IEEE International Conference on Image Processing (ICIP)
Presented at:IEEE International Conference on Image Processing (ICIP) 2017
Project(s): Radio Astronomy Synthesis Imaging 

BibTex PDF 



@inproceedings{lorenz2014sparse,
  title = {A Sparse Kaczmarz Solver and a Linearized Bregman Method for Online Compressed Sensing},
  author = {Lorenz, Dirk and Wenger, Stephan and Sch{\"o}pfer, Frank and Magnor, Marcus},
  booktitle = {Proc. {IEEE} International Conference on Image Processing ({ICIP})},
  pages = {1347--1351},
  month = {Oct},
  year = {2014}
}

Authors