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25 April 2007 Wavelet priors for multiframe image restoration
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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.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Premchandra Shankar and Mark Neifeld "Wavelet priors for multiframe image restoration", Proc. SPIE 6575, Visual Information Processing XVI, 65750D (25 April 2007);

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