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A Semicausal Model for Recursive Filtering of Two-Dimensional Images

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1 Author(s)
Jain, A.K. ; Department of Electrical Engineering, State University of New York

A two-dimensional discrete stochastic model for representing images is developed. This representation has lower mean square error, compared to a standard autoregressive Markov representation. Application of the model to linear filtering of images degraded by white noise leads to scalar recursive filtering equations requiring only 0(N2log2N) computations for N x N images. The filter algorithm is a hybrid algorithm where the image is transformed along one dimension and spatially filtered, recursively, in the other. Examples on a 255 X 255 image are given.

Published in:

Computers, IEEE Transactions on  (Volume:C-26 ,  Issue: 4 )