Multilevel dictionary learning for sparse representation of images

Jayaraman J. Thiagarajan, Karthikeyan N. Ramamurthy, Andreas Spanias

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

31 Scopus citations

Abstract

Adaptive data-driven dictionaries for sparse approximations provide superior performance compared to predefined dictionaries in applications involving representation and classification of data. In this paper, we propose a novel algorithm for learning global dictionaries particularly suited to the sparse representation of natural images. The proposed algorithm uses a hierarchical energy based learning approach to learn a multilevel dictionary. The atoms that contribute the most energy to the representation are learned in the first level and those that contribute lesser energies are learned in the subsequent levels. The learned multilevel dictionary is compared to a dictionary learned using the K-SVD algorithm. Reconstruction results using a small number of non-zero coefficients demonstrate the advantage of exploiting energy hierarchy using multilevel dictionaries, pointing to potential applications in low bit-rate image compression. Superior performance in compressed sensing using optimized sensing matrices with small number of measurements is also demonstrated.

Original languageEnglish (US)
Title of host publication2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings
Pages271-276
Number of pages6
DOIs
StatePublished - Apr 21 2011
Event2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Sedona, AZ, United States
Duration: Jan 4 2011Jan 7 2011

Publication series

Name2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings

Other

Other2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011
CountryUnited States
CitySedona, AZ
Period1/4/111/7/11

Keywords

  • K-hyperline clustering
  • compressed sensing
  • dictionary learning
  • natural image statistics
  • sparse representations

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Education

Fingerprint Dive into the research topics of 'Multilevel dictionary learning for sparse representation of images'. Together they form a unique fingerprint.

  • Cite this

    Thiagarajan, J. J., Ramamurthy, K. N., & Spanias, A. (2011). Multilevel dictionary learning for sparse representation of images. In 2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings (pp. 271-276). [5739224] (2011 Digital Signal Processing and Signal Processing Education Meeting, DSP/SPE 2011 - Proceedings). https://doi.org/10.1109/DSP-SPE.2011.5739224