Title :
A Semicausal Model for Recursive Filtering of Two-Dimensional Images
Author_Institution :
Department of Electrical Engineering, State University of New York
fDate :
4/1/1977 12:00:00 AM
Abstract :
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.
Keywords :
Image processing, Kalman filtering, recursive filtering, two-dimensional filtering, image modelling.; Degradation; Equations; Filtering; Maximum likelihood detection; Mean square error methods; Nonlinear filters; Optical distortion; Statistics; Stochastic processes; Wiener filter; Image processing, Kalman filtering, recursive filtering, two-dimensional filtering, image modelling.;
Journal_Title :
Computers, IEEE Transactions on
DOI :
10.1109/TC.1977.1674844
