Effect of Undersampling on Non-Negative Blind Deconvolution with Autoregressive Filters
Effect of Undersampling on Non-Negative Blind Deconvolution with Autoregressive Filters
复制标题
欠采样对自回归滤波器非负盲反卷积的影响
DOI：
10.1109/icassp40776.2020.9054299
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Piya Pal
中科院分区：
文献类型：
--
作者：
P. Sarangi;Mehmet Can Hücümenoğlu;Piya Pal
研究了输入信号为非负稀疏且未知卷积核为一阶自回归滤波器的盲反卷积问题。我们的目标是了解是否有可能从它们卷积的下采样测量中恢复信号和核。这项工作的动机是钙成像的神经尖峰反褶积问题，其中希望从均匀欠采样测量中以更高的速率恢复尖峰。假设信号是根据伯努利模型生成的，我们证明仅使用O s测量就可以高概率地唯一识别信号和核，其中s是期望的稀疏性。关键思想是利用输入信号的非负约束以及核的参数结构
This paper considers the problem of blind deconvolution where the input signal is non-negative and sparse, and the unknown convolutional kernel is a first order autoregressive filter. Our objective is to understand if it is possible to recover both the signal and the kernel from downsampled measurements of their convolution. This work is motivated by the problem of neural spike deconvolution from calcium imaging, where it is desirable to recover spikes at a higher rate from uniformly undersampled measurements. Assuming that the signals are generated according to a Bernoulli model, we show that it is possible to uniquely identify both the signal and the kernel with high probability using only O s measurements, where s is the expected sparsity. The key p qidea is to exploit non-negative constraints on the input signal as well as the parametric structure of the kernel.1
影响因子：
2.5
作者：
Vogelstein, Joshua T.;Packer, Adam M.;Paninski, Liam
通讯作者：
Paninski, Liam
DOI：
10.1109/tpami.2019.2939237
发表时间：
2019-01
影响因子：
23.6
作者：
Yuqian Zhang;Yenson Lau;Han-Wen Kuo;S. Cheung;A. Pasupathy;John Wright
通讯作者：
Yuqian Zhang;Yenson Lau;Han-Wen Kuo;S. Cheung;A. Pasupathy;John Wright