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Proceedings Paper

Wavelet priors for multiframe image restoration
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Paper Abstract

It is known that the distributions of wavelet coefficients of natural images at different scales and orientations can be approximated by generalized Gaussian probability density functions. We exploit this prior knowledge within a novel statistical framework for multi-frame image restoration based on the maximum a-posteriori (MAP) algorithm. We describe an iterative algorithm for obtaining a high-fidelity object estimate from multiple warped, blurred, and noisy low-resolution images. We compare our new method with several other techniques including linear restoration, and restoration using Markov Random Field (MRF) object priors. We will discuss the performances of the algorithms.

Paper Details

Date Published: 25 April 2007
PDF: 7 pages
Proc. SPIE 6575, Visual Information Processing XVI, 65750D (25 April 2007); doi: 10.1117/12.720939
Show Author Affiliations
Premchandra Shankar, The Univ. of Arizona (United States)
Mark Neifeld, The Univ. of Arizona (United States)

Published in SPIE Proceedings Vol. 6575:
Visual Information Processing XVI
Zia-ur Rahman; Stephen E. Reichenbach; Mark Allen Neifeld, Editor(s)

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