Generalized Graph Spectral Sampling with Stochastic Priors
Generalized Graph Spectral Sampling with Stochastic Priors
复制标题
具有随机先验的广义图谱采样
DOI：
10.1109/icassp40776.2020.9053720
复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
Eldar Yonina C.
中科院分区：
文献类型：
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作者：
Hara Junya;Tanaka Yuichi;Eldar Yonina C.
We consider generalized sampling for stochastic graph signals. The generalized graph sampling framework allows recovery of graph signals beyond the bandlimited setting by placing a correction filter between the sampling and reconstruction operators and assuming an appropriate prior. In this paper, we assume the graph signals are modeled by graph wide sense stationarity (GWSS), which is an extension of WSS for standard time domain signals. Furthermore, sampling is performed in the graph frequency domain along with the assumption that the graph signals lie in a periodic graph spectrum subspace. The correction filter is designed by minimizing the mean-squared error (MSE). The graph spectral response of the correction filter parallels that in generalized sampling for WSS signals. The effectiveness of our approach is validated via experiments by comparing the MSE with existing approaches.
关于图上的信号处理
DOI：
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发表时间：
2012
期刊：
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影响因子：
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作者：
M. Begué
通讯作者：
M. Begué
DOI：
10.1109/icassp.1997.599455
发表时间：
1997
期刊：
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing
影响因子：
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作者：
M. Unser;J. Zerubia
通讯作者：
J. Zerubia
图信号处理 - 对新兴领域的贡献。
DOI：
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发表时间：
2015
期刊：
影响因子：
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作者：
Benjamin Girault
通讯作者：
Benjamin Girault