Browsing by Subject "Signal Filtering and Prediction"

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    3-dimensional median filters for image sequence processing
    (IEEE, 1991-04) Alp, M. Bilge,; Neuvo, Y.
    Two 3-D median-based filtering algorithms have been developed that preserve the motion in the image sequence while attenuating noise effectively. Some observations are made on the root signals in binary domain based on the positive Boolean functions corresponding to the filters. From the Boolean expressions the output distribution functions are derived. The performance of both filters under various noise types is examined theoretically and experimentally. The structures are simulated on a video sequencer (DVSR 100) on real image sequences. Comparisons are made with other 2- and 3-D algorithms from the literature based on mean square error, mean absolute error, and subjective criteria.
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    A general purpose VLSI median filter and its applications for image processing
    (IEEE, 1989) Karaman, Mustafa; Onural, Levent; Atalar, Abdullah
    A general-purpose median filter configuration consisting of two single-chip median filters is proposed. One of the chips is designed for applications requiring variable word-length and variable window size, whereas the other is for real-time applications. The architectures of the chips are based on odd/even transposition sorting. The chips are implemented in 3-μm M2CMOS using full-custom VLSI design techniques. The chips together with a reasonable external hardware can be used for the realizations of many median filtering techniques. The VLSI design procedure of the chips and their applications to different median filtering techniques for image processing are presented.
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    New radix-2-based algorithm for fast median filtering
    (IEEE, 1989) Karaman, M.; Onural, L.
    A fast radi-2-based median filtering algorithm is proposed. The median is determined bit-by-bit successively by eliminating the samples whose previous bits are different to that of the median. The intermediate computations of the algorithm do not involve any array computation, nor any memory. The worst-case computational complexity of the algorithm is O(w) for w samples.