Covariance matrix of a probability distribution for image dictionaries in compressed sensing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sparse representation is one of the principles for the most effective signal processing, and makes it possible for us to infer from less data. The framework of signal processing based on it is called compressed sensing or compressive sensing, where dictionary matrices play an essential role of the basis for sparse representation. In our previous work, we successfully derived an analytical expression of the probability distribution followed by image dictionaries for the images generated by the Gaussian model [1]. However, we have found that the distribution has a difficulty of a divergent covariance matrix, which is needed for an analytical performance evaluation of image processing by compressed sensing. Therefore, it is the purpose of this paper to solve the difficulty and to open the way to the evaluation.

Original languageEnglish
Title of host publicationInternational Conference on Control, Automation and Systems
PublisherIEEE Computer Society
Pages829-832
Number of pages4
Volume2018-October
ISBN (Electronic)9788993215151
Publication statusPublished - Dec 10 2018
Event18th International Conference on Control, Automation and Systems, ICCAS 2018 - PyeongChang, Korea, Republic of
Duration: Oct 17 2018Oct 20 2018

Other

Other18th International Conference on Control, Automation and Systems, ICCAS 2018
CountryKorea, Republic of
CityPyeongChang
Period10/17/1810/20/18

Keywords

  • Compressed sensing
  • Covariance matrix
  • Dictionary matrix

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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