Fast Digital Locally Monotonic Regression - Signal Processing, IEEE Transactions on
نویسنده
چکیده
Locally monotonic regression is the optimal counterpart of iterated median filtering. In a previous paper, Restrepo and Bovik developed an elegant mathematical framework in which they studied locally monotonic regressions in R . The drawback is that the complexity of their algorithms is exponential in N . In this paper, we consider digital locally monotonic regressions, in which the output symbols are drawn from a finite alphabet and, by making a connection to Viterbi decoding, provide a fast O(jAj N ) algorithm that computes any such regression, where jAj is the size of the digital output alphabet, stands for lomo degree, and N is sample size. This is linear in N , and it renders the technique applicable in practice.
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