Multidimensional Gabor-Like Filters Derived from Gaussian Functions on Logarithmic Frequency Axes
This work provides a method for generating filter banks in signal processing, but it appears incremental as it generalizes existing Log-Gabor filters.
The authors tackled the problem of creating filter banks with flexible focus and count by deriving a novel wavelet-like function from Gaussian functions on logarithmic frequency axes, resulting in multidimensional Gabor-like filters that include low-pass filters.
A novel wavelet-like function is presented that makes it convenient to create filter banks given mainly two parameters that influence the focus area and the filter count. This is accomplished by computing the inverse Fourier transform of Gaussian functions on logarithmic frequency axes in the frequency domain. The resulting filters are similar to Gabor filters and represent oriented brief signal oscillations of different sizes. The wavelet-like function can be thought of as a generalized Log-Gabor filter that is multidimensional, always uses Gaussian functions on logarithmic frequency axes, and innately includes low-pass filters from Gaussian functions located at the frequency domain origin.