Directly invertible nonlinear divisive normalization pyramid for image representation

Roberto Valerio, Eero P. Simoncelli, Rafael Navarro

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We present a multiscale nonlinear image representation that permits an efficient coding of natural images. The input image is first decomposed into a set of subbands at multiple scales and orientations using near-orthogonal symmetric quadrature mirror filters. This is followed by a nonlinear "divisive normalization" stage, in which each linear coefficient is divided by a value computed from a small set of neighboring coefficients in space, orientation and scale. This neighborhood is chosen to allow this nonlinear operation to be efficiently inverted. The parameters of the normalization operation are optimized in order to maximize the independence of the normalized responses for natural images. We demonstrate the near-independence of these nonlinear responses, and suggest a number of applications for which this representation should be well suited.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsNarciso Garcia, Luis Salgado, Jose M. Martinez
PublisherSpringer Verlag
Pages331-340
Number of pages10
ISBN (Print)3540200819, 9783540200819
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2849
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Valerio, R., Simoncelli, E. P., & Navarro, R. (2003). Directly invertible nonlinear divisive normalization pyramid for image representation. In N. Garcia, L. Salgado, & J. M. Martinez (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 331-340). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2849). Springer Verlag. https://doi.org/10.1007/978-3-540-39798-4_42