Filters and Matrix Factorization
  We give a number of explicit matrix-algorithms for analysis/synthesis in multi-phase filtering; i.e., the operation on discrete-time signals which allow a separation into frequency-band components, one for each of the ranges of bands, say $N$, starting with low-pass, and then corresponding filtering in the other band-ranges. If there are $N$ bands, the individual filters will be combined into a single matrix action; so a representation of the combined operation on all $N$ bands by an $N \times N$ matrix, where the corresponding matrix-entries are periodic functions; or their extensions to functions of a complex variable. Hence our setting entails a fixed $N \times N$ matrix over a prescribed algebra of functions of a complex variable. In the case of polynomial filters, the factorizations will always be finite. A novelty here is that we allow for a wide family of non-polynomial filter-banks.   Working modulo $N$ in the time domain, our approach also allows for a natural matrix-representation of both down-sampling and up-sampling. The implementation encompasses the combined operation on input, filtering, down-sampling, transmission, up-sampling, an action by dual filters, and synthesis, merges into a single matrix operation. Hence our matrix-factorizations break down the global filtering-process into elementary steps. To accomplish this, we offer a number of adapted matrix factorization-algorithms, such that each factor in our product representation implements in a succession of steps the filtering across pairs of frequency-bands; and so it is of practical significance in implementing signal processing, including filtering of digitized images. Our matrix-factorizations are especially useful in the case of the processing a fixed, but large, number of bands. 
Palle Jorgensen and Myung-Sin Song
math.FA