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- W2170755922 abstract "In this paper, we address the compressive sensing signal reconstruction problem by solving an l 0 -regularized Least Absolute Deviation (LAD) regression problem. A coordinate descent algorithm is developed to solve this l 0 -LAD optimization problem leading to a two-stage operation for signal estimation and basis selection. In the first stage, an estimation of the sparse signal is found by a weighted median operator acting on a shifted-and-scaled version of the measurement samples with weights taken from the entries of the projection matrix. The resultant estimated value is then passed to the second stage that tries to identify whether the corresponding entry is relevant or not. This stage is achieved by a hard threshold operator with adaptable thresholding parameter that is suitably tuned as the algorithm progresses." @default.
- W2170755922 created "2016-06-24" @default.
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- W2170755922 date "2010-01-01" @default.
- W2170755922 modified "2023-09-26" @default.
- W2170755922 title "Compressive sensing signal reconstruction by weighted median regression estimates" @default.
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- W2170755922 doi "https://doi.org/10.1109/icassp.2010.5495738" @default.
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