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JACIII Vol.11 No.3 pp. 308-311
doi: 10.20965/jaciii.2007.p0308
(2007)

Paper:

Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image

Siana Halim

Industrial Engineering Department, Petra Christian University, Indonesia

Received:
April 17, 2006
Accepted:
September 6, 2006
Published:
March 20, 2007
Keywords:
Spatial Autoregressive, Bayesian statistics, Gibbs sampler
Abstract

We apply the Bayesian Spatial Autoregressive, which is developed by Geweke and LeSage, for reducing the blurring effect in the image. This blurring effect, particularly comes from the synthesizing semi regular texture via, e.g., two dimensional block bootstrap. We model the error, i.e., the difference between the true image and the synthesis one, as the Bayesian Spatial Autoregressive (SAR). Moreover, the weight matrix is defined in a specific manner, such that the problem in the computational for a very large matrix can be avoided. Finally, we use the error estimate, as the result of Bayesian SAR modelling, for reducing the blurring effect in the synthesis image.

Cite this article as:
Siana Halim, “Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image,” J. Adv. Comput. Intell. Intell. Inform., Vol.11, No.3, pp. 308-311, 2007.
Data files:
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Last updated on May. 14, 2021