The introduction of helical coordinate system Claerbout (1997) gives us the ability to solve the problem. The Kolmogoroff algorithm of spectral factorization Claerbout (1976) enables us to derive a helix derivative filter from its autocorrelation, the negative Laplacian operator Claerbout (1998b). The Wilson-Burg method of spectral factorization Sava et al. (1998) makes it more convenient to compute the coefficients of a finite-length helix derivative filter.

The helix low-cut filter is another operator useful in enhancing the digital images and also has the circularly symmetric spectrum. Unlike the helix derivative filter, the helix low-cut filter removes the frequency components below an adjustable cut-off frequency from the image, and has a flat response at high frequencies.

In this paper, I present the enhanced helix derivative and low-cut filters with more adjustable parameters. Then I analyze the influence on the filter spectrum for these parameters, and present guidelines for choosing the parameters for image processing, using the examples from GEE.

4/20/1999