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Proceedings Article

Highly overcomplete sparse coding

[+] Author Affiliations
Bruno A. Olshausen

Univ. of California, Berkeley (United States)

Proc. SPIE 8651, Human Vision and Electronic Imaging XVIII, 86510S (March 14, 2013); doi:10.1117/12.2013504
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From Conference Volume 8651

  • Human Vision and Electronic Imaging XVIII
  • Bernice E. Rogowitz; Thrasyvoulos N. Pappas; Huib de Ridder
  • Burlingame, California, USA | February 03, 2013

abstract

This paper explores sparse coding of natural images in the highly overcomplete regime. We show that as the overcompleteness ratio approaches l0x, new types of dictionary elements emerge beyond the classical Gabor function shape obtained from complete or only modestly overcomplete sparse coding. These more diverse dic­ tionaries allow images to be approximated with lower L1 norm (for a fixed SNR), and the coefficients exhibit steeper decay. We also evaluate the learned dictionaries in a denoising task, showing that higher degrees of overcompleteness yield modest gains in peformance. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Bruno A. Olshausen
" Highly overcomplete sparse coding ", Proc. SPIE 8651, Human Vision and Electronic Imaging XVIII, 86510S (March 14, 2013); doi:10.1117/12.2013504; http://dx.doi.org/10.1117/12.2013504


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