Probability distribution of an image dictionary for compressed sensing

Yuhei Ashida, Toshiaki Aida

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

1 Citation (Scopus)

Abstract

Compressed sensing is one of the most effective signal processing methods through the sparse representation of inferred data, in which dictionary matrices play an essential role and they are learned by feature extraction methods such as K-SVD ones. Therefore, in general, it requires a considerable amount of computational cost to construct a dictionary matrix. In this paper, we analytically derive the expression of the probability distribution followed by an image dictionary for compressed sensing, assuming that grey scale images are generated by the Gaussian model. This result enables us to directly generate a dictionary matrix for images with no edge, and can be the first step to analytical performance evaluation of image processing by compressed sensing.

Original languageEnglish
Title of host publicationICCAS 2016 - 2016 16th International Conference on Control, Automation and Systems, Proceedings
PublisherIEEE Computer Society
Pages1377-1380
Number of pages4
ISBN (Electronic)9788993215120
DOIs
Publication statusPublished - Jan 24 2017
Event16th International Conference on Control, Automation and Systems, ICCAS 2016 - Gyeongju, Korea, Republic of
Duration: Oct 16 2016Oct 19 2016

Other

Other16th International Conference on Control, Automation and Systems, ICCAS 2016
CountryKorea, Republic of
CityGyeongju
Period10/16/1610/19/16

Keywords

  • compressed sensing
  • dictionary matrix
  • ratio distribution

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Probability distribution of an image dictionary for compressed sensing'. Together they form a unique fingerprint.

Cite this