Translator Disclaimer
13 November 2003 Adapting overcomplete wavelet models to natural images
Author Affiliations +
Overcomplete wavelet representations have become increasingly popular for their ability to provide highly sparse and robust descriptions of natural signals. We describe a method for incorporating an overcomplete wavelet representation as part of a statistical model of images which includes a sparse prior distribution over the wavelet coefficients. The wavelet basis functions are parameterized by a small set of 2-D functions. These functions are adapted to maximize the average log-likelihood of the model for a large database of natural images. When adapted to natural images, these functions become selective to different spatial orientations, and they achieve a superior degree of sparsity on natural images as compared with traditional wavelet bases. The learned basis is similar to the Steerable Pyramid basis, and yields slightly higher SNR for the same number of active coefficients. Inference with the learned model is demonstrated for applications such as denoising, with results that compare favorably with other methods.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Phil Sallee and Bruno A. Olshausen "Adapting overcomplete wavelet models to natural images", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003);


Contourlets and sparse image expansions
Proceedings of SPIE (November 13 2003)
Practical approach to fractal-based image compression
Proceedings of SPIE (November 01 1991)
Directional multiscale statistical modeling of images
Proceedings of SPIE (November 13 2003)

Back to Top