In this chapter it is shown how convolution kernels can be implemented in practical situations. The material is based on . The presentation is restricted to the case where the desired ideal filter is given in the Fourier domain. An optimal kernel is the set of coefficients that minimizes some distance measure with respect to the ideal filter. A family of distance measures suitable for multidimensional image signals is given. The dependence of attainable error levels on kernel size is demonstrated and convolution results on test images discussed.
KeywordsWeighting Function Test Pattern Reference Function Kernel Size Nyquist Frequency
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