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Benchmarking compressed sensing, super‐resolution, and filter diagonalization
Author(s) -
Markovich Thomas,
Blau Samuel M.,
Sanders Jacob N.,
AspuruGuzik Alán
Publication year - 2016
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.25144
Subject(s) - compressed sensing , nyquist–shannon sampling theorem , filter (signal processing) , computer science , signal (programming language) , algorithm , fourier transform , sampling (signal processing) , benchmarking , resolution (logic) , range (aeronautics) , signal processing , discrete fourier transform (general) , discrete time signal , artificial intelligence , mathematics , digital signal processing , computer vision , short time fourier transform , analog signal , fourier analysis , engineering , signal transfer function , computer hardware , business , aerospace engineering , mathematical analysis , marketing , programming language
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. © 2016 Wiley Periodicals, Inc.