Windowed total variation denoising and noise variance monitoring
We proposed a real time Total-Variation denosing method with an automatic choice of hyper-parameter $\lambda$, and the good performance of this method provides a large application field. In this article, we adapt the developed method to the non stationary signal in using the sliding window, and prop...
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| creator | Liu, Zhanhao Perrodin, Marion Chambrion, Thomas Stoica, Radu |
| description | We proposed a real time Total-Variation denosing method with an automatic
choice of hyper-parameter $\lambda$, and the good performance of this method
provides a large application field. In this article, we adapt the developed
method to the non stationary signal in using the sliding window, and propose a
noise variance monitoring method. The simulated results show that our
proposition follows well the variation of noise variance. |
| doi_str_mv | 10.48550/arxiv.2101.11850 |
| format | Article |
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choice of hyper-parameter $\lambda$, and the good performance of this method
provides a large application field. In this article, we adapt the developed
method to the non stationary signal in using the sliding window, and propose a
noise variance monitoring method. The simulated results show that our
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choice of hyper-parameter $\lambda$, and the good performance of this method
provides a large application field. In this article, we adapt the developed
method to the non stationary signal in using the sliding window, and propose a
noise variance monitoring method. The simulated results show that our
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choice of hyper-parameter $\lambda$, and the good performance of this method
provides a large application field. In this article, we adapt the developed
method to the non stationary signal in using the sliding window, and propose a
noise variance monitoring method. The simulated results show that our
proposition follows well the variation of noise variance.</abstract><doi>10.48550/arxiv.2101.11850</doi><oa>free_for_read</oa></addata></record> |
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| title | Windowed total variation denoising and noise variance monitoring |
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