Robust signal recovery using Bayesian compressed sensing based on Lomax prior
| Autor: | Chaoyu Xia, Jie Yu, Yuxiang Gao, L. Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Noise (signal processing)
Computer science Signal reconstruction Perspective (graphical) 020206 networking & telecommunications 02 engineering and technology Signal Robustness (computer science) Signal Processing Prior probability 0202 electrical engineering electronic engineering information engineering Compressibility 020201 artificial intelligence & image processing Lomax distribution Electrical and Electronic Engineering Algorithm |
| Zdroj: | Signal, Image and Video Processing. 14:1235-1243 |
| ISSN: | 1863-1711 1863-1703 |
| DOI: | 10.1007/s11760-020-01661-z |
| Popis: | Recently published research shows that Lomax distribution exhibits compressibility in Lorentz curves. In this paper, we address the problem of signal reconstruction in the high noise level and phase error environments in a Bayesian framework of Lomax prior distribution. Furthermore, from the perspective of improving sparsity and compressibility of the signal constraints, a novel reconstruction model deducted from Lomax-prior-based Bayesian compressed sensing (LomaxCS) is proposed. The LomaxCS improves the accuracy of existing Bayesian compressed sensing signal reconstruction methods and enhances the robustness against Gauss noise and phase errors. Compared with the conventional models, the proposed LomaxCS model still reveals the general profile of the signal in the worst conditions. The experimental results demonstrate that the proposed algorithm can achieve substantial improvements in terms of recovering signal quality and robustness; meanwhile, it brings an evident application prospect. |
| Databáze: | OpenAIRE |
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