Learning sparse wavelet codes for natural images

Bruno A. Olshausen; Phil Sallee; Michael S. Lewicki

doi:10.1117/12.408604

4 December 2000 Learning sparse wavelet codes for natural images

Bruno A. Olshausen, Phil Sallee, Michael S. Lewicki

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408604
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

Abstract

We show how a wavelet basis may be adapted to best represent natural images in terms of sparse coefficients. The wavelet basis, which may be either complete or overcomplete, is specified by a small number of spatial functions which are repeated across space and combined in a recursive fashion so as to be self-similar across scale. These functions are adapted to minimize the estimated code length under a model that assumes images are composed as a linear superposition of sparse, independent components. When adapted to natural images, the wavelet bases become selective to different spatial orientations, and they achieve a superior degree of sparsity on natural images as compared with traditional wavelet bases.

Citation Download Citation

Bruno A. Olshausen, Phil Sallee, and Michael S. Lewicki "Learning sparse wavelet codes for natural images", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408604

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