In the preceding chapters, the design and analysis of recursive and nonrecur-sive one-dimensional (1-D) and two-dimensional (2-D) digital filters have been discussed for the case of deterministic input signals. However, in situations such as satellite and radar imaging, knowledge of the original image is not deterministically available. Each pixel is considered as a random variable and the image is thought of as a sample of an ensemble of images. For simplicity in modeling, the image is assumed to have a Gaussian distribution which can be specified uniquely by its first- and second-order moments (mean and covariances). To perform the estimation and filtering process, the image should be represented by an appropriate statistical model.
KeywordsCausal Model Image Restoration Colored Noise ARMA Model Spectral Density Function
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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