A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM

被引：1
|
作者：
Huska, Martin [1 ]
Cicone, Antonio [2 ,4 ,5 ]
Kang, Sung Ha [3 ]
Morigi, Serena [1 ]
机构：
[1] Univ Bologna, Dept Math, Bologna, Italy
[2] Univ Aquila, DISIM, Laquila, Italy
[3] Georgia Inst Technol, Sch Math, Atlanta, GA USA
[4] Ist Nazl Geofis & Vulcanol, Rome, Italy
[5] Ist Astrofis & Planetol Spaziali, INAF, Rome, Italy
来源：
IMAGE PROCESSING ON LINE | 2023年 / 13卷
关键词：
signal decomposition; variational model; non-convex optimization; jumps; trend; oscillating signals; ADMM;
D O I：
10.5201/ipol.2023.417
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
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, Os-cillation 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 pro-pose 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.
引用
收藏
页码：153 / 166
页数：14
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