论文标题
适用于压缩传感的可变密度子采样
Adapted variable density subsampling for compressed sensing
论文作者
论文摘要
压缩感测的最新结果表明，最佳的亚采样策略应考虑到手头信号的稀疏模式。这种类似甲骨文的知识即使是理想的，在大多数实际应用中仍然难以捉摸。我们试图通过展示如何通过在稀疏信号的支撑上的概率分布来表征稀疏模式来缩小这一差距，从而使我们再次得出最佳的亚采样策略。该概率分布可以从同一信号类别的信号中很容易估算，从而在数值实验中实现了最先进的性能。我们的方法还扩展到结构化的收购，在此中，采取了测量块，而不是孤立的测量。
Recent results in compressed sensing showed that the optimal subsampling strategy should take into account the sparsity pattern of the signal at hand. This oracle-like knowledge, even though desirable, nevertheless remains elusive in most practical application. We try to close this gap by showing how the sparsity patterns can instead be characterised via a probability distribution on the supports of the sparse signals allowing us to again derive optimal subsampling strategies. This probability distribution can be easily estimated from signals of the same signal class, achieving state of the art performance in numerical experiments. Our approach also extends to structured acquisition, where instead of isolated measurements, blocks of measurements are taken.
