Abstract
We introduce a computationally efficient structured low-rank algorithm for the reconstruction of two-dimensional infrared (2D IR) spectroscopic data from few measurements. The signal is modeled as a combination of exponential lineshapes that are annihilated by appropriately chosen filters. The annihilation relations result in a low-rank constraint on a Toeplitz matrix constructed from signal samples, which is exploited to recover the unknown signal samples. Quantitative and qualitative studies on simulated and experimental data demonstrate that the algorithm outperforms the discrete compressed sensing algorithm, both in uniform and non-uniform sampling settings.
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