Regularized Multichannel Restoration Using Cross-Validation. Zhu, W., Galatsanos, N., & Katsaggelos, A. Graphical Models and Image Processing, 57(1):38–54, Academic Press, jan, 1995. ![Regularized Multichannel Restoration Using Cross-Validation [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Multichannel images are the multiple image planes (channels) obtained by imaging the same scene using multiple sensors. The validity of multichannel restoration where both within- and between-channel relations are incorporated has already been established using both stochastic and deterministic restoration filters. However, it has been demonstrated that stochastic multichannel filters are extremely sensitive to the estimates of the between-channel statistics. In this paper we avoid the problems associated with multichannel stochastic filters by proposing deterministic multichannel filters that do not require any prior knowledge about either the statistics of the multichannel image or the noise. Regularization based on the multichannel cross-validation function is used to obtain these filters. We examine their relation to multichannel linear minimum mean square error restoration filters and we propose a technique to estimate the variance of the noise. Finally, we show experiments where we test the proposed filters and noise variance estimator using color images. © 1994 Academic Press. All rights reserved.
@article{zhu1995regularized,
abstract = {Multichannel images are the multiple image planes (channels) obtained by imaging the same scene using multiple sensors. The validity of multichannel restoration where both within- and between-channel relations are incorporated has already been established using both stochastic and deterministic restoration filters. However, it has been demonstrated that stochastic multichannel filters are extremely sensitive to the estimates of the between-channel statistics. In this paper we avoid the problems associated with multichannel stochastic filters by proposing deterministic multichannel filters that do not require any prior knowledge about either the statistics of the multichannel image or the noise. Regularization based on the multichannel cross-validation function is used to obtain these filters. We examine their relation to multichannel linear minimum mean square error restoration filters and we propose a technique to estimate the variance of the noise. Finally, we show experiments where we test the proposed filters and noise variance estimator using color images. {\textcopyright} 1994 Academic Press. All rights reserved.},
author = {Zhu, W.W. and Galatsanos, N.P. and Katsaggelos, A.K.},
doi = {10.1006/gmip.1995.1005},
institution = {SPIE INTERNATIONAL SOCIETY FOR OPTICAL},
issn = {10773169},
journal = {Graphical Models and Image Processing},
month = {jan},
number = {1},
pages = {38--54},
publisher = {Academic Press},
title = {{Regularized Multichannel Restoration Using Cross-Validation}},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1077316985710052},
volume = {57},
year = {1995}
}
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