@inproceedings{208ffb722fa24294a4b25ddccf4c2fc7,
title = "Wavelet priors for multiframe image restoration",
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.",
keywords = "Multiframe image restoration, Optimal regularization parameters, Superresolution, Wavelet priors",
author = "Premchandra Shankar and Mark Neifeld",
year = "2007",
doi = "10.1117/12.720939",
language = "English (US)",
isbn = "0819466972",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Visual Informaion Processing XVI",
note = "Visual Information Processing XVI ; Conference date: 10-04-2007 Through 10-04-2007",
}