A Semicausal Model for Recursive Filtering of Two-Dimensional Images
作者： Jain
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摘要: 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 linear filtering degraded by white noise leads scalar recursive equations requiring only 0(N2log 2 N) computations N x images. The filter algorithm hybrid where image transformed along one dimension and spatially filtered, recursively, in other. Examples on 255 X are given.
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sci-hub.se 参考文章(26)
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