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Image Estimation Using Doubly Stochastic Gaussian Random Field Models
February 1987 (vol. 9 no. 2)
pp. 245-253
John W. Woods, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180.
Subrahmanyam Dravida, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; AT&T Bell Laboratories, Crawfords Corner Road, Holmdel, NJ 077
Ricardo Mediavilla, Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; AT&T Bell Laboratories, North Andover, MA 01845.
The two-dimensional (2-D) doubly stochastic Gaussian (DSG) model was introduced by one of the authors to provide a complete model for spatial filters which adapt to the local structure in an image signal. Here we present the optimal estimator and 2-D fixed-lag smoother for this DSG model extending earlier work of Ackerson and Fu. As the optimal estimator has an exponentially growing state space, we investigate suboptimal estimators using both a tree and a decision-directed method. Experimental results are presented.
Citation:
John W. Woods, Subrahmanyam Dravida, Ricardo Mediavilla, "Image Estimation Using Doubly Stochastic Gaussian Random Field Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 2, pp. 245-253, Feb. 1987, doi:10.1109/TPAMI.1987.4767898
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