Please use this identifier to cite or link to this item:
http://hdl.handle.net/2122/16718| Authors: | Huska, Martin* Cicone, Antonio* Kang, Sung-Ha* Morigi, Serena* |
Title: | A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM | Journal: | Image Processing On Line | Series/Report no.: | /13 (2023) | Issue Date: | 2023 | DOI: | 10.5201/ipol.2023.417 | Abstract: | We present a thorough implementation of the two-stage framework proposed in [A.Cicone, M.Huska, S.H.Kangand, 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. |
| Appears in Collections: | Article published / in press |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| A Two-stage Signal Decomposition into Jump, Oscillation and Trend using ADMM.pdf | Open Access Published file | 923.92 kB | Adobe PDF | View/Open |
Page view(s)
13
checked on Apr 27, 2024
Download(s)
4
checked on Apr 27, 2024
