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FIGURE 4 Filtering CT data using unsharp masking. The highpass information of the original image (left) is twice as high in the result image (right). Note how details have been amplified. This technique works well due to the lack of noise in the image.

are two main categories of such adaptive filters, filters for image enhancement and filters for image restoration. The two categories mainly differ in the view of the data that is to be filtered. The method of unsharp masking belongs to the first category, image enhancement. The image is made crisper by increasing the image contrast. The input image was not considered to be degraded in any way, and the purpose of the algorithm was just to improve the appearance of the image. In image restoration, as the name implies, the image data are modeled as being degraded by a (normally unknown) process, and the task at hand is to "undo" this degradation and restore the image. The models of image degradation commonly involve random noise processes. Before we introduce the well-known Wiener filter, a short description of stochastic processes in multiple dimensions is given. In multiple dimensions stochastic processes are customary referred to as random fields.

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