Sample-distortion functions for compressed sensing
Sample-distortion functions for compressed sensing
复制标题
DOI：
10.1109/allerton.2011.6120262
复制
发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
M. Davies;Chunli Guo
中科院分区：
文献类型：
--
作者：
M. Davies;Chunli Guo
我们考虑随机环境中的压缩感知，其中感兴趣的信号或图像是从某种意义上可压缩的概率分布中提取的。在这个背景下，我们考虑了I.I.D.的一些样本失真函数。并推导出一个简单的样本失真下限。然后，我们将可压缩模型推广到随机多分辨率图像模型。利用经验样本失真函数，我们能够计算最优的带状采样策略，并准确地预测压缩成像中可能获得的压缩传感性能增益。
We consider compressed sensing within a stochastic setting, where the signal or image of interest is drawn from a probability distribution that is in some sense compressible. Within this setting we consider some sample-distortion functions for i.i.d. compressible distributions and derive a simple sample distortion lower bound. We then extend the compressible model to consider a stochastic multi-resolution image model. Using empirical sample distortion functions we are able to compute an optimal bandwise sampling strategy and to accurately predict the compressed sensing possible performance gains available in compressive imaging.