A Semicausal Model for Recursive Filtering of Two-Dimensional Images
- 1 April 1977
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Computers
- Vol. C-26 (4), 343-350
- https://doi.org/10.1109/tc.1977.1674844
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
This publication has 21 references indexed in Scilit:
- A Fast Karhunen-Loeve Transform for a Class of Random ProcessesIEEE Transactions on Communications, 1976
- Recursive Image Enhancement--Vector ProcessingIEEE Transactions on Communications, 1973
- A dimensionality reducing model for distributed filteringIEEE Transactions on Automatic Control, 1973
- Image restoration: The removal of spatially invariant degradationsProceedings of the IEEE, 1972
- Visual fidelity criterion and modelingProceedings of the IEEE, 1972
- Digital Image Restoration by Constrained DeconvolutionJournal of the Optical Society of America, 1970
- Image restoration for a class of linear spatially-variant degradationsPattern Recognition, 1970
- Linear Least-Squares Filtering of Distorted ImagesJournal of the Optical Society of America, 1967
- Image Restoration by the Method of Least SquaresJournal of the Optical Society of America, 1967
- A Model for the Random Video ProcessBell System Technical Journal, 1966