@inproceedings{49fc5c3a368540008cf77ca2ce8399b9,
title = "Partially linear estimation with application to image deblurring using blurred/noisy image pairs",
abstract = "We address the problem of estimating a random vector X from two sets of measurements Y and Z, such that the estimator is linear in Y. We show that the partially linear minimum mean squared error (PLMMSE) estimator requires knowing only the second-order moments of X and Y, making it of potential interest in various applications. We demonstrate the utility of PLMMSE estimation in recovering a signal, which is sparse in a unitary dictionary, from noisy observations of it and of a filtered version of it. We apply the method to the problem of image enhancement from blurred/noisy image pairs. In this setting the PLMMSE estimator performs better than denoising or deblurring alone, compared to state-of-the-art algorithms. Its performance is slightly worse than joint denoising/deblurring methods, but it runs an order of magnitude faster.",
keywords = "Bayesian estimation, linear estimation, minimum mean squared error",
author = "Tomer Michaeli and Daniel Sigalov and Eldar, \{Yonina C.\} and Fabian Theis and Andrzej Cichocki and Arie Yeredor and Michael Zibulevsky",
year = "2012",
doi = "10.1007/978-3-642-28551-6\_2",
language = "الإنجليزيّة",
isbn = "9783642285509",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "9--16",
booktitle = "Latent Variable Analysis and Signal Separation - 10th International Conference, LVA/ICA 2012, Proceedings",
address = "ألمانيا",
note = "10th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2012 ; Conference date: 12-03-2012 Through 15-03-2012",
}