压缩感知、超分辨率和滤波器对角化的基准测试
Benchmarking Compressed Sensing, Super-Resolution, and Filter Diagonalization
摘要：
已经开发了使用不同策略来绕过奈奎斯特采样定理的信号处理技术，以便恢复比传统的离散傅立叶变换更多的信息。在这里，我们研究三种这样的方法：滤波器对角化，压缩感知和超分辨率。我们将它们应用于科学和工程中常见的各种信号形式，以发现何时以及如何最有效地使用每种方法。我们发现，滤波器对角化为洛伦兹信号提供了最好的结果，而压缩感知和超分辨率对任意信号都有更好的表现。
英文摘要：
Signal processing techniques have been developed that use different strategies to bypass the Nyquist sampling theorem in order to recover more information than a traditional discrete Fourier transform. Here we examine three such methods: filter diagonalization, compressed sensing, and super-resolution. We apply them to a broad range of signal forms commonly found in science and engineering in order to discover when and how each method can be used most profitably. We find that filter diagonalization provides the best results for Lorentzian signals, while compressed sensing and super-resolution perform better for arbitrary signals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
