A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM
Martin Huska, Antonio Cicone, Sung Ha Kang, Serena Morigi
published
2023-05-21
reference
Martin Huska, Antonio Cicone, Sung Ha Kang, and Serena Morigi, A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM, Image Processing On Line, 13 (2023), pp. 153–166. https://doi.org/10.5201/ipol.2023.417

Communicated by Laurent Oudre
Demo edited by Pascal Monasse

Abstract

We present a thorough implementation of the two-stage framework proposed in [A. Cicone, M. Huska, S.H. Kang and S. Morigi, JOT: a Variational Signal Decomposition into Jump, Oscillation and Trend, IEEE Transactions on Signal Processing, 2022]. The method assumes as input a 1D signal represented by a finite-dimensional vector in RN. In the first stage the signal is decomposed into Jump (piece-wise constant), Oscillation, and Trend (smooth) components, and in the second stage the results are refined using residuals of other components. We propose an efficient numerical solution for the first stage based on alternating direction method of multipliers, and a solid algorithm for the solution of the second stage.

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