License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/OASIcs.ICCSW.2013.3
URN: urn:nbn:de:0030-drops-42655
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2013/4265/
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Apostolopoulos, Theofanis

A swarm based heuristic for sparse image recovery

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Abstract

This paper discusses the Compressive Sampling framework as an application for sparse representation (factorization) and recovery of images over an over-complete basis (dictionary). Compressive Sampling is a novel new area which asserts that one can recover images of interest, with much fewer measurements than were originally thought necessary, by searching for the sparsest representation of an image over an over-complete dictionary. This task is achieved by optimizing an objective function that includes two terms: one that measures the image reconstruction error and another that measures the sparsity level. We present and discuss a new swarm based heuristic for sparse image approximation using the Discrete Fourier Transform to enhance its level of sparsity. Our experimental results on reference images demonstrate the good performance of the proposed heuristic over other standard sparse recovery methods (L1-Magic and FOCUSS packages), in a noiseless environment using much fewer measurements. Finally, we discuss possible extensions of the heuristic in noisy environments and weakly sparse images as a realistic improvement with much higher applicability.

BibTeX - Entry

@InProceedings{apostolopoulos:OASIcs:2013:4265,
  author =	{Theofanis Apostolopoulos},
  title =	{{A swarm based heuristic for sparse image recovery}},
  booktitle =	{2013 Imperial College Computing Student Workshop},
  pages =	{3--10},
  series =	{OpenAccess Series in Informatics (OASIcs)},
  ISBN =	{978-3-939897-63-7},
  ISSN =	{2190-6807},
  year =	{2013},
  volume =	{35},
  editor =	{Andrew V. Jones and Nicholas Ng},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2013/4265},
  URN =		{urn:nbn:de:0030-drops-42655},
  doi =		{10.4230/OASIcs.ICCSW.2013.3},
  annote =	{Keywords: Compressive Sampling, sparse image recovery, non-linear programming, sparse repre    sentation, linear inverse problems}
}

Keywords: Compressive Sampling, sparse image recovery, non-linear programming, sparse repre sentation, linear inverse problems
Collection: 2013 Imperial College Computing Student Workshop
Issue Date: 2013
Date of publication: 14.10.2013

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