期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 60, 期 12, 页码 6305-6318出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2012.2214219
关键词
Digital filters; filtering algorithms; jump detection; least squares approximation; nonlinear filters; polynomial smoothing; signal denoising; smoothing methods; sparse derivative; sparse signal; total variation
资金
- NSF [CCF-1018020, CBET 0933531]
- Division of Computing and Communication Foundations
- Direct For Computer & Info Scie & Enginr [1018020] Funding Source: National Science Foundation
This paper addresses the problem of estimating simultaneously a local polynomial signal and an approximately piece-wise constant signal from a noisy additive mixture. The approach developed in this paper synthesizes the total variation filter and least-square polynomial signal smoothing into a unified problem formulation. The method is based on formulating an l(1)-norm regularized inverse problem. A computationally efficient algorithm, based on variable splitting and the alternating direction method of multipliers (ADMM), is presented. Algorithms are derived for both unconstrained and constrained formulations. The method is illustrated on experimental data involving the detection of nano-particles with applications to real-time virus detection using a whispering-gallery mode detector.
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