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• W2896009432 abstract "Recovery of certain piecewise continuous signals from noisy observations has been a major challenge in sciences and engineering. In this paper, in a tight-dimensional representation space, we exploit sparsity hidden in a class of possibly discontinuous signals named finite-dimensional piecewise continuous (FPC) signals. More precisely, we propose a tight-dimensional linear transformation which reveals a certain sparsity in discrete samples of the FPC signals. This transformation is designed by exploiting the fact that most of the consecutive samples are contained in special subspaces. Numerical experiments on recovery of piecewise polynomial signals and piecewise sinusoidal signals show the effectiveness of the revealed sparsity." @default.
• W2896009432 created "2018-10-26" @default.
• W2896009432 creator A5022358441 @default.
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• W2896009432 date "2018-12-15" @default.
• W2896009432 modified "2023-10-17" @default.
• W2896009432 title "Exploiting Sparsity in Tight-Dimensional Spaces for Piecewise Continuous Signal Recovery" @default.
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• W2896009432 doi "https://doi.org/10.1109/tsp.2018.2876328" @default.
• W2896009432 hasPublicationYear "2018" @default.
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