2014/12/09 by Palle E. T. Jørgensen, Palle Jorgensen, Jorgensen, Palle +2
Computer Science · Mathematics · #Digital Filter Design and Implementation #Image and Signal Denoising Methods #Mathematical Analysis and Transform Methods #math.FA #msc:18A32 #msc:42C40 #msc:46M05 #msc:47B10 #msc:60H05 #msc:62M15 #msc:65T60
paper · pdf · doi:10.48550/arxiv.1412.3088
24 pages, the figures are removed, the copy with figures will be available at http://www.siue.edu/~msong/Research/linear.pdf
arxiv created 2014/12/09 · arxiv updated 2014/12/10
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 × 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 × 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.