Filter networks for efficient estimation of local 3-D structure

Linear filtering is a fundamental operation in signal processing, but for multidimensional signals the practical use is severely limited by the computer power available. Decomposition of filters into a layered structure of sparse subfilters, i.e. a filter network, significantly reduces the number of multiplications required for each data sample. A filter network, here used for phase invariant estimation of local 3-D structure, provides a flexible solution for linear filtering, especially suited for applying a set of filters on signals of higher dimensionality. The filter network presented, is twice as efficient as convolution based on the fast Fourier transform (FFT) and outperforms standard convolution by a factor exceeding 50 in terms of multiplications and additions performed.