Nonconvex Energy Minimization with Unsupervised Line Process Classifier for Efficient Piecewise Constant Signals Reconstruction

  • Anass Belcaid Department of Mathematics, School of Artificial Intelligence, Euromed-Fes
  • Mohammed Douimi Department of Mathematics, National School of Arts and Craft-Meknes
Keywords: signal denoisng, edge preserving, DPS algorithm, total variation, energy minimization, unsupervised segmentation.


In this paper, we focus on the problem of signal smoothing and step-detection for piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis, and anomaly detection in genetics. We present a two-stage approach to minimize the well-known line process model which arises from the probabilistic representation of the signal and its segmentation. In the first stage, we minimize a TV least square problem to detect the majority of the continuous edges. In the second stage, we apply a combinatorial algorithm to filter all false jumps introduced by the TV solution. The performances of the proposed method were tested on several synthetic examples. In comparison to recent step-preserving denoising algorithms, the acceleration presents a superior speed and competitive step-detection quality.


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How to Cite
Belcaid, A., & Douimi, M. (2021). Nonconvex Energy Minimization with Unsupervised Line Process Classifier for Efficient Piecewise Constant Signals Reconstruction. Statistics, Optimization & Information Computing, 9(2), 435-452.
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