@inproceedings{dacdcba94b3a4cdea4fd176a2b2b8233,
title = "Spatially constrained Wiener filter with Markov autocorrelation modeling for image resolution enhancement",
abstract = "This paper develops a practical method for image resolution enhancement. The method optimizes the spatially constrained Wiener filter for an efficiently parameterized model of the image autocorrelation based on a Markov random field (MRF) with affine transformation. The paper presents a closed-form solution to parameterize the model for an image. The enhancement method is computationally efficient, because it is formulated as convolution with a small kernel. Because the kernel is small, it can be optimized efficiently and only a small portion of the MRF autocorrelation model is required. Because the autocorrelation model parameters and optimal filter can be computed quickly, the method can be optimized locally for adaptive processing. Experimental results indicate that the new method can balance the error-budget tradeoff between signal error and aliasing error.",
keywords = "Image processing, Interpolation, Markov process, Wiener filter",
author = "Jiazheng Shi and Reichenbach, {Stephen E.}",
year = "2006",
doi = "10.1109/ICIP.2006.313062",
language = "English (US)",
isbn = "1424404819",
series = "Proceedings - International Conference on Image Processing, ICIP",
pages = "2681--2684",
booktitle = "2006 IEEE International Conference on Image Processing, ICIP 2006 - Proceedings",
note = "2006 IEEE International Conference on Image Processing, ICIP 2006 ; Conference date: 08-10-2006 Through 11-10-2006",
}