The application of Markov random field models to wavelet-based image denoising
Imaging and vision systems
Model Building for Random Fields
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
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Random field (RF) models have widespread application in image modeling and analysis. The effectiveness of these models is largely dependent on the choice of neighbor sets, which determine the spatial interactions that are representable by the model. We consider the problem of selecting these neighbor sets for simultaneous autoregressive and Gauss-Markov random field models, based on the correlation structure of the image to be modeled. A procedure for identifying appropriate neighbor sets is proposed, and experimental results which demonstrate the viability of this method are presented